State-of-the-art human pose estimation methods are based on heat map representation. In spite of the good performance, the representation has a few issues in nature, such as non-differentiable postprocessing and quantization error. This work shows that a simple integral operation relates and unifies the heat map representation and joint regression, thus avoiding the above issues. It is differentiable, efficient, and compatible with any heat map based methods. Its effectiveness is convincingly validated via comprehensive ablation experiments under various settings, specifically on 3D pose estimation, for the first time.

This paper proposes learning disentangled but complementary face features with minimal supervision by face identification. Specifically, we construct an identity Distilling and Dispelling Autoencoder (D 2 AE) framework that adversarially learns the identity-distilled features for identity verification and the identity-dispelled features to fool the verification system. Thanks to the design of two-stream cues, the learned disentangled features represent not only the identity or attribute but the complete input image. Comprehensive evaluations further demonstrate that the proposed features not only maintain state-of-the-art identity verification performance on LFW, but also acquire competitive discriminative power for face attribute recognition on CelebA and LFWA. Moreover, the proposed system is ready to semantically control the face generation/editing based on various identities and attributes in an unsupervised manner.

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Since convolutional neural network (CNN) lacks an inherent mechanism to handle large scale variations, we always need to compute feature maps multiple times for multiscale object detection, which has the bottleneck of computational cost in practice. To address this, we devise a recurrent scale approximation (RSA) to compute feature map once only, and only through this map can we approximate the rest maps on other levels. At the core of RSA is the recursive rolling out mechanism: given an initial map at a particular scale, it generates the prediction at a smaller scale that is half the size of input. To further increase efficiency and accuracy, we (a): design a scale-forecast network to globally predict potential scales in the image since there is no need to compute maps on all levels of the pyramid. (b): propose a landmark retracing network (LRN) to trace back locations of the regressed landmarks and generate a confidence score for each landmark; LRN can effectively alleviate false positives caused by the accumulated error in RSA. The whole system can be trained end-to-end in a unified CNN framework. Experiments demonstrate that our proposed algorithm is superior against state-of-the-art methods on face detection benchmarks and achieves comparable results for generic proposal generation. The source code of our system is available. 1 .

Objects obscured by occluders are considered lost in the images acquired by conventional camera systems, prohibiting both visualization and understanding of such hidden objects. Non-line-of-sight methods (NLOS) aim at recovering information about hidden scenes, which could help make medical imaging less invasive, improve the safety of autonomous vehicles, and potentially enable capturing unprecedented high-definition RGB-D data sets that include geometry beyond the directly visible parts. Recent NLOS methods have demonstrated scene recovery from time-resolved pulse-illuminated measurements encoding occluded objects as faint indirect reflections. Unfortunately, these systems are fundamentally limited by the quartic intensity fall-off for diffuse scenes. With laser illumination limited by eye-safety limits, recovery algorithms must tackle this challenge by incorporating scene priors. However, existing NLOS reconstruction algorithms do not facilitate learning scene priors. Even if they did, datasets that allow for such supervision do not exist, and successful encoder-decoder networks and generative adversarial networks fail for real-world NLOS data. In this work, we close this gap by learning hidden scene feature representations tailored to both reconstruction and recognition tasks such as classification or object detection, while still relying on physical models at the feature level. We overcome the lack of real training data with a generalizable architecture that can be trained in simulation. We learn the differentiable scene representation jointly with the reconstruction task using a differentiable transient renderer in the objective, and demonstrate that it generalizes to unseen classes and unseen real-world scenes , unlike existing encoder-decoder architectures and generative adversarial networks. The proposed method allows for end-to-end training for different NLOS tasks , such as image reconstruction, classification, and object detection, while being memory-efficient and running at real-time rates. We demonstrate hidden view synthesis, RGB-D reconstruction, classification, and object detection in the hidden scene in an end-to-end fashion.

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Despite recent success of deep learning models in numerous applications, their widespread use on mobile devices is seriously impeded by storage and computational requirements. In this paper, we propose a novel network compression method called Adaptive Dimension Adjustment Tucker decomposition (ADA-Tucker). With learnable core tensors and transformation matrices, ADA-Tucker performs Tucker decomposition of arbitrary-order tensors. Furthermore, we propose that weight tensors in networks with proper order and balanced dimension are easier to be compressed. Therefore, the high flexibility in decomposition choice distinguishes ADA-Tucker from all previous low-rank models. To compress more, we further extend the model to Shared Core ADA-Tucker (SCADA-Tucker) by defining a shared core tensor for all layers. Our methods require no overhead of recording indices of non-zero elements. Without loss of accuracy, our methods reduce the storage of LeNet-5 and LeNet-300 by ratios of 691× and 233×, respectively, significantly outperforming state of the art. The effectiveness of our methods is also evaluated on other three benchmarks (CIFAR-10, SVHN, ILSVRC12) and modern newly deep networks (ResNet, Wide-ResNet). × × × × ≈ × Figure 1: Four categories of mainstream compression methods, which can be combined for higher compression ratio. "a×" etc. means that the network is compressed by a times. Reducing the bits of weight representation and Effective coding There are two approaches for the first category: clustering and quantization. BinaryConnect Courbariaux et al. (2015) enforces weights in neural networks to take binary values. Incremental network quantization Zhou et al. (2017) quantizes deep models with 5 bits incrementally. Gong et al. Gong et al. (2015) learn CNNs in advance, and then apply k-means clustering on the weights for quantization. Ullrich et al. Ullrich et al. (2017) cluster the weights with a Gaussian mixture model (GMM), using only six class centers to represent all weights. The second category, effective coding, always combines with the first category, where the coding scheme is mainly Huffman coding. DeepCompression Han et al. (2016) first introduces Huffman coding in network compression and improves the compression ratios further. CNNPack Wang et al. (2016) also uses Huffman coding and gets better results. Making weights sparse Sparsity can be induced in either the original domain or the frequency domain. The most commonly used sparsity method in the original domain is pruning. Han et al. Han et al. (2015) recursively train a neural network and prune unimportant connections based on their weight magnitude. Dynamic network surgery Guo et al. (2016) prunes and splices the branch of the network. The frequency domain sparsity methods benefit from discrete cosine transformation (DCT). Chen et al. Chen et al. (2016) take advantage of DCT to make weights sparse in the frequency domain. Wang et al. Wang et al. (2016) combine DCT, clustering and Huffman coding for further compression. Simplifying the network structur...

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