DeepLab
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1. 1 DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs LiangChieh Chen, George Papandreou, Senior Member, IEEE, Iasonas Kokkinos, Member, IEEE, Kevin Murphy, and Alan L. Yuille, Fellow, IEEE Abstract—In this work we address the task of semantic image segmentation with Deep Learning and make three main contributions arXiv:1606.00915v2 [cs.CV] 12 May 2017 that are experimentally shown to have substantial practical merit. First, we highlight convolution with upsampled filters, or ‘atrous convolution’, as a powerful tool in dense prediction tasks. Atrous convolution allows us to explicitly control the resolution at which feature responses are computed within Deep Convolutional Neural Networks. It also allows us to effectively enlarge the field of view of filters to incorporate larger context without increasing the number of parameters or the amount of computation. Second, we propose atrous spatial pyramid pooling (ASPP) to robustly segment objects at multiple scales. ASPP probes an incoming convolutional feature layer with filters at multiple sampling rates and effective fieldsofviews, thus capturing objects as well as image context at multiple scales. Third, we improve the localization of object boundaries by combining methods from DCNNs and probabilistic graphical models. The commonly deployed combination of maxpooling and downsampling in DCNNs achieves invariance but has a toll on localization accuracy. We overcome this by combining the responses at the final DCNN layer with a fully connected Conditional Random Field (CRF), which is shown both qualitatively and quantitatively to improve localization performance. Our proposed “DeepLab” system sets the new stateofart at the PASCAL VOC2012 semantic image segmentation task, reaching 79.7% mIOU in the test set, and advances the results on three other datasets: PASCALContext, PASCALPersonPart, and Cityscapes. All of our code is made publicly available online. Index Terms—Convolutional Neural Networks, Semantic Segmentation, Atrous Convolution, Conditional Random Fields. ✦ 1 I NTRODUCTION Deep Convolutional Neural Networks (DCNNs) [1] have employed in a fully convolutional fashion [14]. In order to pushed the performance of computer vision systems to overcome this hurdle and efficiently produce denser feature soaring heights on a broad array of highlevel problems, maps, we remove the downsampling operator from the last including image classification [2], [3], [4], [5], [6] and object few max pooling layers of DCNNs and instead upsample detection [7], [8], [9], [10], [11], [12], where DCNNs trained the filters in subsequent convolutional layers, resulting in in an endtoend manner have delivered strikingly better feature maps computed at a higher sampling rate. Filter results than systems relying on handcrafted features. Es upsampling amounts to inserting holes (‘trous’ in French) sential to this success is the builtin invariance of DCNNs between nonzero filter taps. This technique has a long to local image transformations, which allows them to learn history in signal processing, originally developed for the increasingly abstract data representations [13]. This invari efficient computation of the undecimated wavelet transform ance is clearly desirable for classification tasks, but can ham in a scheme also known as “algorithme a` trous” [15]. We use per dense prediction tasks such as semantic segmentation, the term atrous convolution as a shorthand for convolution where abstraction of spatial information is undesired. with upsampled filters. Various flavors of this idea have In particular we consider three challenges in the applica been used before in the context of DCNNs by [3], [6], [16]. tion of DCNNs to semantic image segmentation: (1) reduced In practice, we recover full resolution feature maps by a feature resolution, (2) existence of objects at multiple scales, combination of atrous convolution, which computes feature and (3) reduced localization accuracy due to DCNN invari maps more densely, followed by simple bilinear interpola ance. Next, we discuss these challenges and our approach tion of the feature responses to the original image size. This to overcome them in our proposed DeepLab system. scheme offers a simple yet powerful alternative to using The first challenge is caused by the repeated combination deconvolutional layers [13], [14] in dense prediction tasks. of maxpooling and downsampling (‘striding’) performed at Compared to regular convolution with larger filters, atrous consecutive layers of DCNNs originally designed for image convolution allows us to effectively enlarge the field of view classification [2], [4], [5]. This results in feature maps with of filters without increasing the number of parameters or the significantly reduced spatial resolution when the DCNN is amount of computation. The second challenge is caused by the existence of ob • L.C. Chen, G. Papandreou, and K. Murphy are with Google Inc. I. Kokki jects at multiple scales. A standard way to deal with this is nos is with University College London. A. Yuille is with the Departments to present to the DCNN rescaled versions of the same image of Cognitive Science and Computer Science, Johns Hopkins University. and then aggregate the feature or score maps [6], [17], [18]. The first two authors contributed equally to this work. We show that this approach indeed increases the perfor
2. 2 mance of our system, but comes at the cost of computing The updated DeepLab system we present in this paper feature responses at all DCNN layers for multiple scaled features several improvements compared to its first version versions of the input image. Instead, motivated by spatial reported in our original conference publication [38]. Our pyramid pooling [19], [20], we propose a computationally new version can better segment objects at multiple scales, efficient scheme of resampling a given feature layer at via either multiscale input processing [17], [39], [40] or multiple rates prior to convolution. This amounts to probing the proposed ASPP. We have built a residual net variant the original image with multiple filters that have com of DeepLab by adapting the stateofart ResNet [11] image plementary effective fields of view, thus capturing objects classification DCNN, achieving better semantic segmenta as well as useful image context at multiple scales. Rather tion performance compared to our original model based than actually resampling features, we efficiently implement on VGG16 [4]. Finally, we present a more comprehensive this mapping using multiple parallel atrous convolutional experimental evaluation of multiple model variants and layers with different sampling rates; we call the proposed report stateofart results not only on the PASCAL VOC technique “atrous spatial pyramid pooling” (ASPP). 2012 benchmark but also on other challenging tasks. We The third challenge relates to the fact that an object have implemented the proposed methods by extending the centric classifier requires invariance to spatial transforma Caffe framework [41]. We share our code and models at tions, inherently limiting the spatial accuracy of a DCNN. a companion web site http://liangchiehchen.com/projects/ One way to mitigate this problem is to use skiplayers DeepLab.html. to extract “hypercolumn” features from multiple network layers when computing the final segmentation result [14], [21]. Our work explores an alternative approach which we 2 R ELATED W ORK show to be highly effective. In particular, we boost our Most of the successful semantic segmentation systems de model’s ability to capture fine details by employing a fully veloped in the previous decade relied on handcrafted fea connected Conditional Random Field (CRF) [22]. CRFs have tures combined with flat classifiers, such as Boosting [24], been broadly used in semantic segmentation to combine [42], Random Forests [43], or Support Vector Machines [44]. class scores computed by multiway classifiers with the low Substantial improvements have been achieved by incorpo level information captured by the local interactions of pixels rating richer information from context [45] and structured and edges [23], [24] or superpixels [25]. Even though works prediction techniques [22], [26], [27], [46], but the perfor of increased sophistication have been proposed to model mance of these systems has always been compromised by the hierarchical dependency [26], [27], [28] and/or high the limited expressive power of the features. Over the past order dependencies of segments [29], [30], [31], [32], [33], few years the breakthroughs of Deep Learning in image we use the fully connected pairwise CRF proposed by [22] classification were quickly transferred to the semantic seg for its efficient computation, and ability to capture fine edge mentation task. Since this task involves both segmentation details while also catering for long range dependencies. and classification, a central question is how to combine the That model was shown in [22] to improve the performance two tasks. of a boostingbased pixellevel classifier. In this work, we The first family of DCNNbased systems for seman demonstrate that it leads to stateoftheart results when tic segmentation typically employs a cascade of bottom coupled with a DCNNbased pixellevel classifier. up image segmentation, followed by DCNNbased region A highlevel illustration of the proposed DeepLab model classification. For instance the bounding box proposals and is shown in Fig. 1. A deep convolutional neural network masked regions delivered by [47], [48] are used in [7] and (VGG16 [4] or ResNet101 [11] in this work) trained in [49] as inputs to a DCNN to incorporate shape information the task of image classification is repurposed to the task into the classification process. Similarly, the authors of [50] of semantic segmentation by (1) transforming all the fully rely on a superpixel representation. Even though these connected layers to convolutional layers (i.e., fully convo approaches can benefit from the sharp boundaries delivered lutional network [14]) and (2) increasing feature resolution by a good segmentation, they also cannot recover from any through atrous convolutional layers, allowing us to compute of its errors. feature responses every 8 pixels instead of every 32 pixels in The second family of works relies on using convolution the original network. We then employ bilinear interpolation ally computed DCNN features for dense image labeling, to upsample by a factor of 8 the score map to reach the and couples them with segmentations that are obtained original image resolution, yielding the input to a fully independently. Among the first have been [39] who apply connected CRF [22] that refines the segmentation results. DCNNs at multiple image resolutions and then employ a From a practical standpoint, the three main advantages segmentation tree to smooth the prediction results. More of our DeepLab system are: (1) Speed: by virtue of atrous recently, [21] propose to use skip layers and concatenate the convolution, our dense DCNN operates at 8 FPS on an computed intermediate feature maps within the DCNNs for NVidia Titan X GPU, while Mean Field Inference for the pixel classification. Further, [51] propose to pool the inter fullyconnected CRF requires 0.5 secs on a CPU. (2) Accu mediate feature maps by region proposals. These works still racy: we obtain stateofart results on several challenging employ segmentation algorithms that are decoupled from datasets, including the PASCAL VOC 2012 semantic seg the DCNN classifier’s results, thus risking commitment to mentation benchmark [34], PASCALContext [35], PASCAL premature decisions. PersonPart [36], and Cityscapes [37]. (3) Simplicity: our sys The third family of works uses DCNNs to directly tem is composed of a cascade of two very wellestablished provide dense categorylevel pixel labels, which makes modules, DCNNs and CRFs. it possible to even discard segmentation altogether. The
3. 3 Input Aeroplane Coarse DCNN Score map Atrous Convolution Final Output Fully Connected CRF Bilinear Interpolation Fig. 1: Model Illustration. A Deep Convolutional Neural Network such as VGG16 or ResNet101 is employed in a fully convolutional fashion, using atrous convolution to reduce the degree of signal downsampling (from 32x down 8x). A bilinear interpolation stage enlarges the feature maps to the original image resolution. A fully connected CRF is then applied to refine the segmentation result and better capture the object boundaries. segmentationfree approaches of [14], [52] directly apply high level of activity in the benchmark’s leaderboard1 [17], DCNNs to the whole image in a fully convolutional fashion, [40], [58], [59], [60], [61], [62], [63]. Interestingly, most top transforming the last fully connected layers of the DCNN performing methods have adopted one or both of the key into convolutional layers. In order to deal with the spatial lo ingredients of our DeepLab system: Atrous convolution for calization issues outlined in the introduction, [14] upsample efficient dense feature extraction and refinement of the raw and concatenate the scores from intermediate feature maps, DCNN scores by means of a fully connected CRF. We outline while [52] refine the prediction result from coarse to fine by below some of the most important and interesting advances. propagating the coarse results to another DCNN. Our work Endtoend training for structured prediction has more re builds on these works, and as described in the introduction cently been explored in several related works. While we extends them by exerting control on the feature resolution, employ the CRF as a postprocessing method, [40], [59], introducing multiscale pooling techniques and integrating [62], [64], [65] have successfully pursued joint learning of the densely connected CRF of [22] on top of the DCNN. the DCNN and CRF. In particular, [59], [65] unroll the CRF We show that this leads to significantly better segmentation meanfield inference steps to convert the whole system into results, especially along object boundaries. The combination an endtoend trainable feedforward network, while [62] of DCNN and CRF is of course not new but previous works approximates one iteration of the dense CRF mean field only tried locally connected CRF models. Specifically, [53] inference [22] by convolutional layers with learnable filters. use CRFs as a proposal mechanism for a DCNNbased Another fruitful direction pursued by [40], [66] is to learn reranking system, while [39] treat superpixels as nodes for a the pairwise terms of a CRF via a DCNN, significantly local pairwise CRF and use graphcuts for discrete inference. improving performance at the cost of heavier computation. As such their models were limited by errors in superpixel In a different direction, [63] replace the bilateral filtering computations or ignored longrange dependencies. Our ap module used in mean field inference with a faster domain proach instead treats every pixel as a CRF node receiving transform module [67], improving the speed and lowering unary potentials by the DCNN. Crucially, the Gaussian CRF the memory requirements of the overall system, while [18], potentials in the fully connected CRF model of [22] that we [68] combine semantic segmentation with edge detection. adopt can capture longrange dependencies and at the same Weaker supervision has been pursued in a number of time the model is amenable to fast mean field inference. papers, relaxing the assumption that pixellevel semantic We note that mean field inference had been extensively annotations are available for the whole training set [58], [69], studied for traditional image segmentation tasks [54], [55], [70], [71], achieving significantly better results than weakly [56], but these older models were typically limited to short supervised preDCNN systems such as [72]. In another line range connections. In independent work, [57] use a very of research, [49], [73] pursue instance segmentation, jointly similar densely connected CRF model to refine the results of tackling object detection and semantic segmentation. DCNN for the problem of material classification. However, What we call here atrous convolution was originally de the DCNN module of [57] was only trained by sparse point veloped for the efficient computation of the undecimated supervision instead of dense supervision at every pixel. wavelet transform in the “algorithme a` trous” scheme of [15]. We refer the interested reader to [74] for early refer Since the first version of this work was made publicly ences from the wavelet literature. Atrous convolution is also available [38], the area of semantic segmentation has pro intimately related to the “noble identities” in multirate sig gressed drastically. Multiple groups have made important nal processing, which builds on the same interplay of input advances, significantly raising the bar on the PASCAL VOC 1. http://host.robots.ox.ac.uk:8080/leaderboard/displaylb.php? 2012 semantic segmentation benchmark, as reflected to the challengeid=11&compid=6
4. 4 signal and filter sampling rates [75]. Atrous convolution is a Output feature term we first used in [6]. The same operation was later called Convolution dilated convolution by [76], a term they coined motivated by kernel = 3 stride = 1 the fact that the operation corresponds to regular convolu pad = 1 tion with upsampled (or dilated in the terminology of [15]) Input feature filters. Various authors have used the same operation before (a) Sparse feature extraction for denser feature extraction in DCNNs [3], [6], [16]. Beyond mere resolution enhancement, atrous convolution allows us Convolution kernel = 3 to enlarge the field of view of filters to incorporate larger stride = 1 pad = 2 context, which we have shown in [38] to be beneficial. This rate = 2 approach has been pursued further by [76], who employ a (insert 1 zero) rate = 2 series of atrous convolutional layers with increasing rates to aggregate multiscale context. The atrous spatial pyramid (b) Dense feature extraction pooling scheme proposed here to capture multiscale objects and context also employs multiple atrous convolutional Fig. 2: Illustration of atrous convolution in 1D. (a) Sparse layers with different sampling rates, which we however lay feature extraction with standard convolution on a low reso out in parallel instead of in serial. Interestingly, the atrous lution input feature map. (b) Dense feature extraction with convolution technique has also been adopted for a broader atrous convolution with rate r = 2, applied on a high set of tasks, such as object detection [12], [77], instance resolution input feature map. level segmentation [78], visual question answering [79], and optical flow [80]. We also show that, as expected, integrating into DeepLab more advanced image classification DCNNs such as the residual net of [11] leads to better results. This has also been observed independently by [81]. 3 M ETHODS downsampling convolution upsampling stride= 2 kernel=7 stride=2 3.1 Atrous Convolution for Dense Feature Extraction and FieldofView Enlargement The use of DCNNs for semantic segmentation, or other atrous convolution dense prediction tasks, has been shown to be simply and kernel=7 rate= 2 successfully addressed by deploying DCNNs in a fully stride=1 convolutional fashion [3], [14]. However, the repeated com bination of maxpooling and striding at consecutive layers Fig. 3: Illustration of atrous convolution in 2D. Top row: of these networks reduces significantly the spatial resolution sparse feature extraction with standard convolution on a of the resulting feature maps, typically by a factor of 32 low resolution input feature map. Bottom row: Dense fea across each direction in recent DCNNs. A partial remedy ture extraction with atrous convolution with rate r = 2, is to use ‘deconvolutional’ layers as in [14], which however applied on a high resolution input feature map. requires additional memory and time. We advocate instead the use of atrous convolution, originally developed for the efficient computation of the We illustrate the algorithm’s operation in 2D through a undecimated wavelet transform in the “algorithme a` trous” simple example in Fig. 3: Given an image, we assume that scheme of [15] and used before in the DCNN context by [3], we first have a downsampling operation that reduces the [6], [16]. This algorithm allows us to compute the responses resolution by a factor of 2, and then perform a convolution of any layer at any desirable resolution. It can be applied with a kernel  here, the vertical Gaussian derivative. If one posthoc, once a network has been trained, but can also be implants the resulting feature map in the original image seamlessly integrated with training. coordinates, we realize that we have obtained responses at Considering onedimensional signals first, the output only 1/4 of the image positions. Instead, we can compute y[i] of atrous convolution 2 of a 1D input signal x[i] with a responses at all image positions if we convolve the full filter w[k] of length K is defined as: resolution image with a filter ‘with holes’, in which we up K sample the original filter by a factor of 2, and introduce zeros y[i] = x[i + r · k]w[k]. (1) in between filter values. Although the effective filter size k=1 increases, we only need to take into account the nonzero filter values, hence both the number of filter parameters and The rate parameter r corresponds to the stride with which the number of operations per position stay constant. The we sample the input signal. Standard convolution is a resulting scheme allows us to easily and explicitly control special case for rate r = 1. See Fig. 2 for illustration. the spatial resolution of neural network feature responses. 2. We follow the standard practice in the DCNN literature and use In the context of DCNNs one can use atrous convolution nonmirrored filters in this definition. in a chain of layers, effectively allowing us to compute the
5. 5 final DCNN network responses at an arbitrarily high resolu Conv Conv Conv Conv tion. For example, in order to double the spatial density of kernel: 3x3 kernel: 3x3 kernel: 3x3 kernel: 3x3 computed feature responses in the VGG16 or ResNet101 rate: 6 rate: 12 rate: 18 rate: 24 rate = 24 networks, we find the last pooling or convolutional layer rate = 12 rate = 18 rate = 6 that decreases resolution (’pool5’ or ’conv5 1’ respectively), set its stride to 1 to avoid signal decimation, and replace all subsequent convolutional layers with atrous convolutional layers having rate r = 2. Pushing this approach all the way through the network could allow us to compute feature Atrous Spatial Pyramid Pooling responses at the original image resolution, but this ends Input Feature Map up being too costly. We have adopted instead a hybrid approach that strikes a good efficiency/accuracy tradeoff, using atrous convolution to increase by a factor of 4 the Fig. 4: Atrous Spatial Pyramid Pooling (ASPP). To classify density of computed feature maps, followed by fast bilinear the center pixel (orange), ASPP exploits multiscale features interpolation by an additional factor of 8 to recover feature by employing multiple parallel filters with different rates. maps at the original image resolution. Bilinear interpolation The effective FieldOfViews are shown in different colors. is sufficient in this setting because the class score maps (corresponding to logprobabilities) are quite smooth, as illustrated in Fig. 5. Unlike the deconvolutional approach 3.2 Multiscale Image Representations using Atrous adopted by [14], the proposed approach converts image Spatial Pyramid Pooling classification networks into dense feature extractors without DCNNs have shown a remarkable ability to implicitly repre requiring learning any extra parameters, leading to faster sent scale, simply by being trained on datasets that contain DCNN training in practice. objects of varying size. Still, explicitly accounting for object scale can improve the DCNN’s ability to successfully handle Atrous convolution also allows us to arbitrarily enlarge both large and small objects [6]. the fieldofview of filters at any DCNN layer. Stateofthe We have experimented with two approaches to han art DCNNs typically employ spatially small convolution dling scale variability in semantic segmentation. The first kernels (typically 3×3) in order to keep both computation approach amounts to standard multiscale processing [17], and number of parameters contained. Atrous convolution [18]. We extract DCNN score maps from multiple (three with rate r introduces r − 1 zeros between consecutive filter in our experiments) rescaled versions of the original image values, effectively enlarging the kernel size of a k ×k filter using parallel DCNN branches that share the same param to ke = k + (k − 1)(r − 1) without increasing the number eters. To produce the final result, we bilinearly interpolate of parameters or the amount of computation. It thus offers the feature maps from the parallel DCNN branches to the an efficient mechanism to control the fieldofview and original image resolution and fuse them, by taking at each finds the best tradeoff between accurate localization (small position the maximum response across the different scales. fieldofview) and context assimilation (large fieldofview). We do this both during training and testing. Multiscale We have successfully experimented with this technique: processing significantly improves performance, but at the Our DeepLabLargeFOV model variant [38] employs atrous cost of computing feature responses at all DCNN layers for convolution with rate r = 12 in VGG16 ‘fc6’ layer with multiple scales of input. significant performance gains, as detailed in Section 4. The second approach is inspired by the success of the RCNN spatial pyramid pooling method of [20], which showed that regions of an arbitrary scale can be accurately Turning to implementation aspects, there are two effi and efficiently classified by resampling convolutional fea cient ways to perform atrous convolution. The first is to tures extracted at a single scale. We have implemented a implicitly upsample the filters by inserting holes (zeros), or variant of their scheme which uses multiple parallel atrous equivalently sparsely sample the input feature maps [15]. convolutional layers with different sampling rates. The fea We implemented this in our earlier work [6], [38], followed tures extracted for each sampling rate are further processed by [76], within the Caffe framework [41] by adding to the in separate branches and fused to generate the final result. im2col function (it extracts vectorized patches from multi The proposed “atrous spatial pyramid pooling” (DeepLab channel feature maps) the option to sparsely sample the ASPP) approach generalizes our DeepLabLargeFOV vari underlying feature maps. The second method, originally ant and is illustrated in Fig. 4. proposed by [82] and used in [3], [16] is to subsample the input feature map by a factor equal to the atrous convolu tion rate r, deinterlacing it to produce r2 reduced resolution 3.3 Structured Prediction with FullyConnected Condi maps, one for each of the r×r possible shifts. This is followed tional Random Fields for Accurate Boundary Recovery by applying standard convolution to these intermediate A tradeoff between localization accuracy and classifica feature maps and reinterlacing them to the original image tion performance seems to be inherent in DCNNs: deeper resolution. By reducing atrous convolution into regular con models with multiple maxpooling layers have proven most volution, it allows us to use offtheshelf highly optimized successful in classification tasks, however the increased in convolution routines. We have implemented the second variance and the large receptive fields of toplevel nodes can approach into the TensorFlow framework [83]. only yield smooth responses. As illustrated in Fig. 5, DCNN
6. 6 The pairwise potential has a form that allows for efficient inference while using a fullyconnected graph, i.e. when connecting all pairs of image pixels, i, j . In particular, as in [22], we use the following expression: pi − pj 2 Ii − Ij 2 Image/G.T. DCNN output CRF Iteration 1 CRF Iteration 2 CRF Iteration 10 θij (xi , xj ) = µ(xi , xj ) w1 exp − − 2σα2 2σβ2 Fig. 5: Score map (input before softmax function) and belief pi − pj 2 map (output of softmax function) for Aeroplane. We show +w2 exp − (3) the score (1st row) and belief (2nd row) maps after each 2σγ2 mean field iteration. The output of last DCNN layer is used where µ(xi , xj ) = 1 if xi = xj , and zero otherwise, which, as input to the mean field inference. as in the Potts model, means that only nodes with dis tinct labels are penalized. The remaining expression uses two Gaussian kernels in different feature spaces; the first, score maps can predict the presence and rough position of ‘bilateral’ kernel depends on both pixel positions (denoted objects but cannot really delineate their borders. as p) and RGB color (denoted as I ), and the second kernel Previous work has pursued two directions to address only depends on pixel positions. The hyper parameters σα , this localization challenge. The first approach is to harness σβ and σγ control the scale of Gaussian kernels. The first information from multiple layers in the convolutional net kernel forces pixels with similar color and position to have work in order to better estimate the object boundaries [14], similar labels, while the second kernel only considers spatial [21], [52]. The second is to employ a superpixel represen proximity when enforcing smoothness. tation, essentially delegating the localization task to a low Crucially, this model is amenable to efficient approxi level segmentation method [50]. mate probabilistic inference [22]. The message passing up We pursue an alternative direction based on coupling dates under a fully decomposable mean field approximation the recognition capacity of DCNNs and the finegrained b(x) = i bi (xi ) can be expressed as Gaussian convolutions localization accuracy of fully connected CRFs and show in bilateral space. Highdimensional filtering algorithms that it is remarkably successful in addressing the localiza [84] significantly speedup this computation resulting in an tion challenge, producing accurate semantic segmentation algorithm that is very fast in practice, requiring less that 0.5 results and recovering object boundaries at a level of detail sec on average for Pascal VOC images using the publicly that is well beyond the reach of existing methods. This available implementation of [22]. direction has been extended by several followup papers [17], [40], [58], [59], [60], [61], [62], [63], [65], since the first version of our work was published [38]. 4 E XPERIMENTAL R ESULTS Traditionally, conditional random fields (CRFs) have We finetune the model weights of the Imagenetpretrained been employed to smooth noisy segmentation maps [23], VGG16 or ResNet101 networks to adapt them to the [31]. Typically these models couple neighboring nodes, fa semantic segmentation task in a straightforward fashion, voring samelabel assignments to spatially proximal pixels. following the procedure of [14]. We replace the 1000way Qualitatively, the primary function of these shortrange Imagenet classifier in the last layer with a classifier having as CRFs is to clean up the spurious predictions of weak classi many targets as the number of semantic classes of our task fiers built on top of local handengineered features. (including the background, if applicable). Our loss function Compared to these weaker classifiers, modern DCNN is the sum of crossentropy terms for each spatial position architectures such as the one we use in this work pro in the CNN output map (subsampled by 8 compared to duce score maps and semantic label predictions which are the original image). All positions and labels are equally qualitatively different. As illustrated in Fig. 5, the score weighted in the overall loss function (except for unlabeled maps are typically quite smooth and produce homogeneous pixels which are ignored). Our targets are the ground truth classification results. In this regime, using shortrange CRFs labels (subsampled by 8). We optimize the objective function can be detrimental, as our goal should be to recover detailed with respect to the weights at all network layers by the local structure rather than further smooth it. Using contrast standard SGD procedure of [2]. We decouple the DCNN sensitive potentials [23] in conjunction to localrange CRFs and CRF training stages, assuming the DCNN unary terms can potentially improve localization but still miss thin are fixed when setting the CRF parameters. structures and typically requires solving an expensive dis We evaluate the proposed models on four challenging crete optimization problem. datasets: PASCAL VOC 2012, PASCALContext, PASCAL To overcome these limitations of shortrange CRFs, we PersonPart, and Cityscapes. We first report the main results integrate into our system the fully connected CRF model of of our conference version [38] on PASCAL VOC 2012, and [22]. The model employs the energy function move forward to latest results on all datasets. E(x) = θi (xi ) + θij (xi , xj ) (2) i ij 4.1 PASCAL VOC 2012 where x is the label assignment for pixels. We use as unary Dataset: The PASCAL VOC 2012 segmentation benchmark potential θi (xi ) = − log P (xi ), where P (xi ) is the label [34] involves 20 foreground object classes and one back assignment probability at pixel i as computed by a DCNN. ground class. The original dataset contains 1, 464 (train),
7. 7 Kernel Rate FOV Params Speed bef/aft CRF Learning policy Batch size Iteration mean IOU 7×7 4 224 134.3M 1.44 64.38 / 67.64 step 30 6K 62.25 4×4 4 128 65.1M 2.90 59.80 / 63.74 4×4 8 224 65.1M 2.90 63.41 / 67.14 poly 30 6K 63.42 3×3 12 224 20.5M 4.84 62.25 / 67.64 poly 30 10K 64.90 poly 10 10K 64.71 poly 10 20K 65.88 TABLE 1: Effect of FieldOfView by adjusting the kernel size and atrous sampling rate r at ‘fc6’ layer. We show TABLE 2: PASCAL VOC 2012 val set results (%) (before CRF) number of model parameters, training speed (img/sec), and as different learning hyper parameters vary. Employing val set mean IOU before and after CRF. DeepLabLargeFOV “poly” learning policy is more effective than “step” when (kernel size 3×3, r = 12) strikes the best balance. training DeepLabLargeFOV. 1, 449 (val), and 1, 456 (test) pixellevel labeled images for 4.1.2 Improvements after conference version of this work training, validation, and testing, respectively. The dataset is augmented by the extra annotations provided by [85], After the conference version of this work [38], we have resulting in 10, 582 (trainaug) training images. The perfor pursued three main improvements of our model, which we mance is measured in terms of pixel intersectionoverunion discuss below: (1) different learning policy during training, (IOU) averaged across the 21 classes. (2) atrous spatial pyramid pooling, and (3) employment of deeper networks and multiscale processing. 4.1.1 Results from our conference version Learning rate policy: We have explored different learn We employ the VGG16 network pretrained on Imagenet, ing rate policies when training DeepLabLargeFOV. Similar adapted for semantic segmentation as described in Sec to [86], we also found that employing a “poly” learning rate iter power tion 3.1. We use a minibatch of 20 images and initial policy (the learning rate is multiplied by (1− max iter ) ) learning rate of 0.001 (0.01 for the final classifier layer), is more effective than “step” learning rate (reduce the multiplying the learning rate by 0.1 every 2000 iterations. learning rate at a fixed step size). As shown in Tab. 2, We use momentum of 0.9 and weight decay of 0.0005. employing “poly” (with power = 0.9) and using the same After the DCNN has been finetuned on trainaug, we batch size and same training iterations yields 1.17% better crossvalidate the CRF parameters along the lines of [22]. We performance than employing “step” policy. Fixing the batch use default values of w2 = 3 and σγ = 3 and we search for size and increasing the training iteration to 10K improves the best values of w1 , σα , and σβ by crossvalidation on 100 the performance to 64.90% (1.48% gain); however, the total images from val. We employ a coarsetofine search scheme. training time increases due to more training iterations. We The initial search range of the parameters are w1 ∈ [3 : 6], then reduce the batch size to 10 and found that comparable σα ∈ [30 : 10 : 100] and σβ ∈ [3 : 6] (MATLAB notation), performance is still maintained (64.90% vs. 64.71%). In the and then we refine the search step sizes around the first end, we employ batch size = 10 and 20K iterations in order round’s best values. We employ 10 mean field iterations. to maintain similar training time as previous “step” policy. Field of View and CRF: In Tab. 1, we report experiments Surprisingly, this gives us the performance of 65.88% (3.63% with DeepLab model variants that use different fieldof improvement over “step”) on val, and 67.7% on test, com view sizes, obtained by adjusting the kernel size and atrous pared to 65.1% of the original “step” setting for DeepLab sampling rate r in the ‘fc6’ layer, as described in Sec. 3.1. LargeFOV before CRF. We employ the “poly” learning rate We start with a direct adaptation of VGG16 net, using policy for all experiments reported in the rest of the paper. the original 7 × 7 kernel size and r = 4 (since we use Atrous Spatial Pyramid Pooling: We have experimented no stride for the last two maxpooling layers). This model with the proposed Atrous Spatial Pyramid Pooling (ASPP) yields performance of 67.64% after CRF, but is relatively scheme, described in Sec. 3.1. As shown in Fig. 7, ASPP slow (1.44 images per second during training). We have for VGG16 employs several parallel fc6fc7fc8 branches. improved model speed to 2.9 images per second by re They all use 3×3 kernels but different atrous rates r in the ducing the kernel size to 4 × 4. We have experimented ‘fc6’ in order to capture objects of different size. In Tab. 3, with two such network variants with smaller (r = 4) and we report results with several settings: (1) Our baseline larger (r = 8) FOV sizes; the latter one performs better. LargeFOV model, having a single branch with r = 12, Finally, we employ kernel size 3×3 and even larger atrous (2) ASPPS, with four branches and smaller atrous rates sampling rate (r = 12), also making the network thinner by (r = {2, 4, 8, 12}), and (3) ASPPL, with four branches retaining a random subset of 1,024 out of the 4,096 filters and larger rates (r = {6, 12, 18, 24}). For each variant in layers ‘fc6’ and ‘fc7’. The resulting model, DeepLabCRF we report results before and after CRF. As shown in the LargeFOV, matches the performance of the direct VGG16 table, ASPPS yields 1.22% improvement over the baseline adaptation (7 × 7 kernel size, r = 4). At the same time, LargeFOV before CRF. However, after CRF both LargeFOV DeepLabLargeFOV is 3.36 times faster and has significantly and ASPPS perform similarly. On the other hand, ASPPL fewer parameters (20.5M instead of 134.3M). yields consistent improvements over the baseline LargeFOV The CRF substantially boosts performance of all model both before and after CRF. We evaluate on test the proposed variants, offering a 35% absolute increase in mean IOU. ASPPL + CRF model, attaining 72.6%. We visualize the Test set evaluation: We have evaluated our DeepLab effect of the different schemes in Fig. 8. CRFLargeFOV model on the PASCAL VOC 2012 official Deeper Networks and Multiscale Processing: We have test set. It achieves 70.3% mean IOU performance. experimented building DeepLab around the recently pro
8. 8 Fig. 6: PASCAL VOC 2012 val results. Input image and our DeepLab results before/after CRF. SumFusion Fc8 (1x1) Fc8 Fc8 Fc8 Fc8 (1x1) (1x1) (1x1) (1x1) Fc7 (1x1) Fc7 Fc7 Fc7 Fc7 (1x1) (1x1) (1x1) (1x1) Fc6 (3x3, rate = 12) Fc6 Fc6 Fc6 Fc6 (3x3, rate = 6) (3x3, rate = 12) (3x3, rate = 18) (3x3, rate = 24) (a) Image (b) LargeFOV (c) ASPPS (d) ASPPL Pool5 Pool5 Fig. 8: Qualitative segmentation results with ASPP com (a) DeepLabLargeFOV (b) DeepLabASPP pared to the baseline LargeFOV model. The ASPPL model, employing multiple large FOVs can successfully capture Fig. 7: DeepLabASPP employs multiple filters with differ objects as well as image context at multiple scales. ent rates to capture objects and context at multiple scales. Method before CRF after CRF LargeFOV 65.76 69.84 ilar to what we did for VGG16 net, we repurpose ResNet ASPPS 66.98 69.73 101 by atrous convolution, as described in Sec. 3.1. On top of ASPPL 68.96 71.57 that, we adopt several other features, following recent work of [17], [18], [39], [40], [58], [59], [62]: (1) Multiscale inputs: TABLE 3: Effect of ASPP on PASCAL VOC 2012 val set per We separately feed to the DCNN images at scale = {0.5, 0.75, formance (mean IOU) for VGG16 based DeepLab model. 1}, fusing their score maps by taking the maximum response LargeFOV: single branch, r = 12. ASPPS: four branches, r across scales for each position separately [17]. (2) Models = {2, 4, 8, 12}. ASPPL: four branches, r = {6, 12, 18, 24}. pretrained on MSCOCO [87]. (3) Data augmentation by randomly scaling the input images (from 0.5 to 1.5) during MSC COCO Aug LargeFOV ASPP CRF mIOU training. In Tab. 4, we evaluate how each of these factors, 68.72 along with LargeFOV and atrous spatial pyramid pooling 71.27 (ASPP), affects val set performance. Adopting ResNet101 73.28 instead of VGG16 significantly improves DeepLab perfor 74.87 75.54 mance (e.g., our simplest ResNet101 based model attains 76.35 68.72%, compared to 65.76% of our DeepLabLargeFOV 77.69 VGG16 based variant, both before CRF). Multiscale fusion [17] brings extra 2.55% improvement, while pretraining TABLE 4: Employing ResNet101 for DeepLab on PASCAL the model on MSCOCO gives another 2.01% gain. Data VOC 2012 val set. MSC: Employing mutliscale inputs with augmentation during training is effective (about 1.6% im max fusion. COCO: Models pretrained on MSCOCO. Aug: provement). Employing LargeFOV (adding an atrous con Data augmentation by randomly rescaling inputs. volutional layer on top of ResNet, with 3×3 kernel and rate = 12) is beneficial (about 0.6% improvement). Further 0.8% improvement is achieved by atrous spatial pyramid pooling posed residual net ResNet101 [11] instead of VGG16. Sim (ASPP). Postprocessing our best model by dense CRF yields
9. 9 performance of 77.69%. Qualitative results: We provide qualitative visual com parisons of DeepLab’s results (our best model variant) before and after CRF in Fig. 6. The visualization results obtained by DeepLab before CRF already yields excellent segmentation results, while employing the CRF further im Image VGG16 Bef. VGG16 Aft. ResNet Bef. ResNet Aft. proves the performance by removing false positives and refining object boundaries. Fig. 9: DeepLab results based on VGG16 net or ResNet Test set results: We have submitted the result of our 101 before and after CRF. The CRF is critical for accurate final best model to the official server, obtaining test set prediction along object boundaries with VGG16, whereas performance of 79.7%, as shown in Tab. 5. The model ResNet101 has acceptable performance even before CRF. substantially outperforms previous DeepLab variants (e.g., DeepLabLargeFOV with VGG16 net) and is currently the 75 top performing method on the PASCAL VOC 2012 segmen 70 tation leaderboard. mean IOU (%) 65 60 Method mIOU 55 ResNet aft VGG−16 aft DeepLabCRFLargeFOVCOCO [58] 72.7 50 ResNet bef VGG−16 bef MERL DEEP GCRF [88] 73.2 45 0 5 10 15 20 25 30 35 40 Trimap Width (pixels) CRFRNN [59] 74.7 POSTECH DeconvNet CRF VOC [61] 74.8 (a) (b) BoxSup [60] 75.2 Context + CRFRNN [76] 75.3 QO4mres [66] 75.5 Fig. 10: (a) Trimap examples (topleft: image. topright: DeepLabCRFAttention [17] 75.7 groundtruth. bottomleft: trimap of 2 pixels. bottomright: CentraleSuperBoundaries++ [18] 76.0 trimap of 10 pixels). (b) Pixel mean IOU as a function of the DeepLabCRFAttentionDT [63] 76.3 HReNet + DenseCRF [89] 76.8 band width around the object boundaries when employing LRR 4x COCO [90] 76.8 VGG16 or ResNet101 before and after CRF. DPN [62] 77.5 Adelaide Context [40] 77.8 Oxford TVG HO CRF [91] 77.9 Method MSC COCO Aug LargeFOV ASPP CRF mIOU Context CRF + Guidance CRF [92] 78.1 VGG16 Adelaide VeryDeep FCN VOC [93] 79.1 DeepLab [38] 37.6 DeepLabCRF (ResNet101) 79.7 DeepLab [38] 39.6 ResNet101 TABLE 5: Performance on PASCAL VOC 2012 test set. We DeepLab 39.6 have added some results from recent arXiv papers on top of DeepLab 41.4 DeepLab 42.9 the official leadearboard results. DeepLab 43.5 DeepLab 44.7 VGG16 vs. ResNet101: We have observed that DeepLab 45.7 DeepLab based on ResNet101 [11] delivers better segmen O2 P [45] 18.1 tation results along object boundaries than employing VGG CFM [51] 34.4 16 [4], as visualized in Fig. 9. We think the identity mapping FCN8s [14] 37.8 CRFRNN [59] 39.3 [94] of ResNet101 has similar effect as hypercolumn fea ParseNet [86] 40.4 tures [21], which exploits the features from the intermediate BoxSup [60] 40.5 layers to better localize boundaries. We further quantize this HO CRF [91] 41.3 Context [40] 43.3 effect in Fig. 10 within the “trimap” [22], [31] (a narrow band VeryDeep [93] 44.5 along object boundaries). As shown in the figure, employing ResNet101 before CRF has almost the same accuracy along TABLE 6: Comparison with other stateofart methods on object boundaries as employing VGG16 in conjunction with PASCALContext dataset. a CRF. Postprocessing the ResNet101 result with a CRF further improves the segmentation result. DeepLab improves 2% over the VGG16 LargeFOV. Simi 4.2 PASCALContext lar to [17], employing multiscale inputs and maxpooling Dataset: The PASCALContext dataset [35] provides de to merge the results improves the performance to 41.4%. tailed semantic labels for the whole scene, including both Pretraining the model on MSCOCO brings extra 1.5% object (e.g., person) and stuff (e.g., sky). Following [35], the improvement. Employing atrous spatial pyramid pooling proposed models are evaluated on the most frequent 59 is more effective than LargeFOV. After further employing classes along with one background category. The training dense CRF as post processing, our final model yields 45.7%, set and validation set contain 4998 and 5105 images. outperforming the current stateofart method [40] by 2.4% Evaluation: We report the evaluation results in Tab. 6. without using their nonlinear pairwise term. Our final Our VGG16 based LargeFOV variant yields 37.6% before model is slightly better than the concurrent work [93] by and 39.6% after CRF. Repurposing the ResNet101 [11] for 1.2%, which also employs atrous convolution to repurpose
10. 10 Fig. 11: PASCALContext results. Input image, groundtruth, and our DeepLab results before/after CRF. Method MSC COCO Aug LFOV ASPP CRF mIOU Method mIOU ResNet101 prerelease version of dataset DeepLab 58.90 Adelaide Context [40] 66.4 DeepLab 63.10 FCN8s [14] 65.3 DeepLab 64.40 DeepLab 64.94 DeepLabCRFLargeFOVStrongWeak [58] 64.8 DeepLabCRFLargeFOV [38] 63.1 DeepLab 62.18 DeepLab 62.76 CRFRNN [59] 62.5 DPN [62] 59.1 Attention [17] 56.39 Segnet basic [100] 57.0 HAZN [95] 57.54 Segnet extended [100] 56.1 LGLSTM [96] 57.97 Graph LSTM [97] 60.16 official version Adelaide Context [40] 71.6 Dilation10 [76] 67.1 TABLE 7: Comparison with other stateofart methods on DPN [62] 66.8 PASCALPersonPart dataset. Pixellevel Encoding [101] 64.3 DeepLabCRF (ResNet101) 70.4 the residual net of [11] for semantic segmentation. TABLE 8: Test set results on the Cityscapes dataset, compar Qualitative results: We visualize the segmentation re ing our DeepLab system with other stateofart methods. sults of our best model with and without CRF as post pro cessing in Fig. 11. DeepLab before CRF can already predict most of the object/stuff with high accuracy. Employing CRF, DeepLab alone yields 58.9%, significantly outperforming our model is able to further remove isolated false positives DeepLabLargeFOV (VGG16 net) and DeepLabAttention and improve the prediction along object/stuff boundaries. (VGG16 net) by about 7% and 2.5%, respectively. Incorpo rating multiscale inputs and fusion by maxpooling further improves performance to 63.1%. Additionally pretraining 4.3 PASCALPersonPart the model on MSCOCO yields another 1.3% improvement. Dataset: We further perform experiments on semantic part However, we do not observe any improvement when adopt segmentation [98], [99], using the extra PASCAL VOC 2010 ing either LargeFOV or ASPP on this dataset. Employing annotations by [36]. We focus on the person part for the the dense CRF to post process our final output substantially dataset, which contains more training data and large varia outperforms the concurrent work [97] by 4.78%. tion in object scale and human pose. Specifically, the dataset Qualitative results: We visualize the results in Fig. 12. contains detailed part annotations for every person, e.g. eyes, nose. We merge the annotations to be Head, Torso, Upper/Lower Arms and Upper/Lower Legs, resulting in 4.4 Cityscapes six person part classes and one background class. We only Dataset: Cityscapes [37] is a recently released largescale use those images containing persons for training (1716 im dataset, which contains high quality pixellevel annotations ages) and validation (1817 images). of 5000 images collected in street scenes from 50 different Evaluation: The human part segmentation results on cities. Following the evaluation protocol [37], 19 semantic PASCALPersonPart is reported in Tab. 7. [17] has already labels (belonging to 7 super categories: ground, construc conducted experiments on this dataset with repurposed tion, object, nature, sky, human, and vehicle) are used for VGG16 net for DeepLab, attaining 56.39% (with multiscale evaluation (the void label is not considered for evaluation). inputs). Therefore, in this part, we mainly focus on the effect The training, validation, and test sets contain 2975, 500, and of repurposing ResNet101 for DeepLab. With ResNet101, 1525 images respectively.
11. 11 Fig. 12: PASCALPersonPart results. Input image, groundtruth, and our DeepLab results before/after CRF. Fig. 13: Cityscapes results. Input image, groundtruth, and our DeepLab results before/after CRF. Full Aug LargeFOV ASPP CRF mIOU plored the validation set in Tab. 9. The images of Cityscapes VGG16 have resolution 2048×1024, making it a challenging prob 62.97 lem to train deeper networks with limited GPU memory. 64.18 During benchmarking the prerelease of the dataset, we 64.89 65.94 downsampled the images by 2. However, we have found that it is beneficial to process the images in their original ResNet101 66.6 resolution. With the same training protocol, using images 69.2 of original resolution significantly brings 1.9% and 1.8% 70.4 improvements before and after CRF, respectively. In order 71.0 71.4 to perform inference on this dataset with high resolution images, we split each image into overlapped regions, similar to [37]. We have also replaced the VGG16 net with ResNet TABLE 9: Val set results on Cityscapes dataset. Full: model 101. We do not exploit multiscale inputs due to the lim trained with full resolution images. ited GPU memories at hand. Instead, we only explore (1) deeper networks (i.e., ResNet101), (2) data augmentation, Test set results of prerelease: We have participated in (3) LargeFOV or ASPP, and (4) CRF as post processing benchmarking the Cityscapes dataset prerelease. As shown on this dataset. We first find that employing ResNet101 in the top of Tab. 8, our model attained third place, with per alone is better than using VGG16 net. Employing LargeFOV formance of 63.1% and 64.8% (with training on additional brings 2.6% improvement and using ASPP further improves coarsely annotated images). results by 1.2%. Adopting data augmentation and CRF as post processing brings another 0.6% and 0.4%, respectively. Val set results: After the initial release, we further ex
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14. 14 [91] A. Arnab, S. Jayasumana, S. Zheng, and P. Torr, “Higher order Iasonas Kokkinos (S’02–M’06) obtained the potentials in endtoend trainable conditional random fields,” Diploma of Engineering in 2001 and the Ph.D. arXiv:1511.08119, 2015. Degree in 2006 from the School of Electrical and [92] F. Shen and G. Zeng, “Fast semantic image segmentation with Computer Engineering of the National Technical high order context and guided filtering,” arXiv:1605.04068, 2016. University of Athens in Greece, and the Habili [93] Z. Wu, C. Shen, and A. van den Hengel, “Bridging tation Degree in 2013 from Universit ParisEst. categorylevel and instancelevel semantic image segmentation,” In 2006 he joined the University of California at arXiv:1605.06885, 2016. Los Angeles as a postdoctoral scholar, and in [94] K. He, X. Zhang, S. Ren, and J. Sun, “Identity mappings in deep 2008 joined as faculty the Department of Applied residual networks,” arXiv:1603.05027, 2016. Mathematics of Ecole Centrale Paris (Centrale [95] F. Xia, P. Wang, L.C. Chen, and A. L. Yuille, “Zoom better to Supelec), working an associate professor in the see clearer: Huamn part segmentation with auto zoom net,” Center for Visual Computing of CentraleSupelec and affiliate researcher arXiv:1511.06881, 2015. at INRIASaclay. In 2016 he joined University College London and Face [96] X. Liang, X. Shen, D. Xiang, J. Feng, L. Lin, and S. Yan, “Se book Artificial Intelligence Research. His currently research activity is on mantic object parsing with localglobal long shortterm memory,” deep learning for computer vision, focusing in particular on structured arXiv:1511.04510, 2015. prediction for deep learning, shape modeling, and multitask learning [97] X. Liang, X. Shen, J. Feng, L. Lin, and S. Yan, “Semantic object architectures. He has been awarded a young researcher grant by the parsing with graph lstm,” arXiv:1603.07063, 2016. French National Research Agency, has served as associate editor for [98] J. Wang and A. Yuille, “Semantic part segmentation using com the Image and Vision Computing and Computer Vision and Image positional model combining shape and appearance,” in CVPR, Understanding Journals, serves regularly as a reviewer and area chair 2015. for all major computer vision conferences and journals. [99] P. Wang, X. Shen, Z. Lin, S. Cohen, B. Price, and A. Yuille, “Joint object and part segmentation using deep learned potentials,” in ICCV, 2015. [100] V. Badrinarayanan, A. Kendall, and R. Cipolla, “Segnet: A deep convolutional encoderdecoder architecture for image segmenta tion,” arXiv:1511.00561, 2015. [101] J. Uhrig, M. Cordts, U. Franke, and T. Brox, “Pixellevel en coding and depth layering for instancelevel semantic labeling,” arXiv:1604.05096, 2016. [102] O. Ronneberger, P. Fischer, and T. Brox, “Unet: Convolutional Kevin Murphy was born in Ireland, grew up in networks for biomedical image segmentation,” in MICCAI, 2015. England, went to graduate school in the USA (MEng from U. Penn, PhD from UC Berkeley, Postdoc at MIT), and then became a professor at the Computer Science and Statistics Depart ments at the University of British Columbia in Vancouver, Canada in 2004. After getting tenure, Kevin went to Google in Mountain View, Cali fornia for his sabbatical. In 2011, he converted LiangChieh Chen received his B.Sc. from Na to a fulltime research scientist at Google. Kevin tional Chiao Tung University, Taiwan, his M.S. has published over 50 papers in refereed con from the University of Michigan Ann Arbor, and ferences and journals related to machine learning and graphical mod his Ph.D. from the University of California Los els. He has recently published an 1100page textbook called “Machine Angeles. He is currently working at Google. His Learning: a Probabilistic Perspective” (MIT Press, 2012). research interests include semantic image seg mentation, probabilistic graphical models, and machine learning. Alan L. Yuille (F’09) received the BA degree in math ematics from the University of Cambridge in 1976. His PhD on theoretical physics, super vised by Prof. S.W. Hawking, was approved in George Papandreou (S’03–M’09–SM’14) holds 1981. He was a research scientist in the Artificial a Diploma (2003) and a Ph.D. (2009) in Elec Intelligence Laboratory at MIT and the Division trical Engineering and Computer Science, both of Applied Sciences at Harvard University from from the National Technical University of Athens 1982 to 1988. He served as an assistant and (NTUA), Greece. He is currently a Research Sci associate professor at Harvard until 1996. He entist at Google, following appointments as Re was a senior research scientist at the Smith search Assistant Professor at the Toyota Tech Kettlewell Eye Research Institute from 1996 to nological Institute at Chicago (20132014) and 2002. He joined the University of California, Los Angeles, as a full Postdoctoral Research Scholar at the University professor with a joint appointment in statistics and psychology in 2002, of California, Los Angeles (20092013). and computer science in 2007. He was appointed a Bloomberg Dis His research interests are in computer vision tinguished Professor at Johns Hopkins University in January 2016. He and machine learning, with a current emphasis on deep learning. He holds a joint appointment between the Departments of Cognitive science regularly serves as a reviewer and program committee member to the and Computer Science. His research interests include computational main journals and conferences in computer vision, image processing, models of vision, mathematical models of cognition, and artificial intelli and machine learning. He has been a coorganizer of the NIPS 2012, gence and neural network 2013, and 2014 Workshops on Perturbations, Optimization, and Statis tics and coeditor of a book on the same topic (MIT Press, 2016).

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