mtybilly/Instruct2DS huggingface dataset:
SSNet: Scale Selection Network for Online 3D Action Prediction
Authors: Jun Liu, Amir Shahroudy, Gang Wang, Ling-Yu Duan, Alex C. Kot
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018 (Main Conference Track), 2018

Abstract: In action prediction (early action recognition), the goal is to predict the class label of an ongoing action using its observed part so far. In this paper, we focus on online action prediction in streaming 3D skeleton sequences. A dilated convolutional network is introduced to model the motion dynamics in temporal dimension via a sliding window over the time axis. As there are significant temporal scale variations of the observed part of the ongoing action at different progress levels, we propose a novel window scale selection scheme to make our network focus on the performed part of the ongoing action and try to suppress the noise from the previous actions at each time step. Furthermore, an activation sharing scheme is proposed to deal with the overlapping computations among the adjacent steps, which allows our model to run more efficiently. The extensive experiments on two challenging datasets show the effectiveness of the proposed action prediction framework.
Detecting and Recognizing Human-Object Interactions
Authors: Georgia Gkioxari, Ross Girshick, Piotr Dollár, Kaiming He
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018 (Main Conference Track), 2018

Abstract: To understand the visual world, a machine must not only recognize individual object instances but also how they interact. Humans are often at the center of such interactions and detecting human-object interactions is an important practical and scientific problem. In this paper, we address the task of detecting (human, verb, object) triplets in challenging everyday photos. We propose a novel model that is driven by a human-centric approach. Our hypothesis is that the appearance of a person -- their pose, clothing, action -- is a powerful cue for localizing the objects they are interacting with. To exploit this cue, our model learns to predict an action-specific density over target object locations based on the appearance of a detected person. Our model also jointly learns to detect people and objects, and by fusing these predictions it efficiently infers interaction triplets in a clean, jointly trained end-to-end system we call InteractNet. We validate our approach on the recently introduced Verbs in COCO (V-COCO) and HICO-DET datasets, where we show quantitatively compelling results.
Unsupervised Learning and Segmentation of Complex Activities From Video
Authors: Fadime Sener, Angela Yao
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018 (Main Conference Track), 2018

Abstract: This paper presents a new method for unsupervised segmentation of complex activities from video into multiple steps, or sub-activities, without any textual input. We propose an iterative discriminative-generative approach which alternates between discriminatively learning the appearance of sub-activities from the videos' visual features to sub-activity labels and generatively modelling the temporal structure of sub-activities using a Generalized Mallows Model. In addition, we introduce a model for background to account for frames unrelated to the actual activities. Our approach is validated on the challenging Breakfast Actions and Inria Instructional Videos datasets and outperforms both unsupervised and weakly-supervised state of the art.
Unsupervised Training for 3D Morphable Model Regression
Authors: Kyle Genova, Forrester Cole, Aaron Maschinot, Aaron Sarna, Daniel Vlasic, William T. Freeman
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018 (Main Conference Track), 2018

Abstract: We present a method for training a regression network from image pixels to 3D morphable model coordinates using only unlabeled photographs. The training loss is based on features from a facial recognition network, computed on-the-fly by rendering the predicted faces with a differentiable renderer. To make training from features feasible and avoid network fooling effects, we introduce three objectives: a batch distribution loss that encourages the output distribution to match the distribution of the morphable model, a loopback loss that ensures the network can correctly reinterpret its own output, and a multi-view identity loss that compares the features of the predicted 3D face and the input photograph from multiple viewing angles. We train a regression network using these objectives, a set of unlabeled photographs, and the morphable model itself, and demonstrate state-of-the-art results.
Video Based Reconstruction of 3D People Models
Authors: Thiemo Alldieck, Marcus Magnor, Weipeng Xu, Christian Theobalt, Gerard Pons-Moll
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018 (Main Conference Track), 2018

Abstract: This paper describes how to obtain accurate 3D body models and texture of arbitrary people from a single, monocular video in which a person is moving. Based on a parametric body model, we present a robust processing pipeline achieving 3D model fits with 5mm accuracy also for clothed people. Our main contribution is a method to nonrigidly deform the silhouette cones corresponding to the dynamic human silhouettes, resulting in a visual hull in a common reference frame that enables surface reconstruction. This enables efficient estimation of a consensus 3D shape, texture and implanted animation skeleton based on a large number of frames. We present evaluation results for a number of test subjects and analyze overall performance. Requiring only a smartphone or webcam, our method enables everyone to create their own fully animatable digital double, e.g., for social VR applications or virtual try-on for online fashion shopping.
Pose-Guided Photorealistic Face Rotation
Authors: Yibo Hu, Xiang Wu, Bing Yu, Ran He, Zhenan Sun
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018 (Main Conference Track), 2018

Abstract: Face rotation provides an effective and cheap way for data augmentation and representation learning of face recognition. It is a challenging generative learning problem due to the large pose discrepancy between two face images. This work focuses on flexible face rotation of arbitrary head poses, including extreme profile views. We propose a novel Couple-Agent Pose-Guided Generative Adversarial Network (CAPG-GAN) to generate both neutral and profile head pose face images. The head pose information is encoded by facial landmark heatmaps. It not only forms a mask image to guide the generator in learning process but also provides a flexible controllable condition during inference. A couple-agent discriminator is introduced to reinforce on the realism of synthetic arbitrary view faces. Besides the generator and conditional adversarial loss, CAPG-GAN further employs identity preserving loss and total variation regularization to preserve identity information and refine local textures respectively. Quantitative and qualitative experimental results on the Multi-PIE and LFW databases consistently show the superiority of our face rotation method over the state-of-the-art.
Mesoscopic Facial Geometry Inference Using Deep Neural Networks
Authors: Loc Huynh, Weikai Chen, Shunsuke Saito, Jun Xing, Koki Nagano, Andrew Jones, Paul Debevec, Hao Li
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018 (Main Conference Track), 2018

Abstract: We present a learning-based approach for synthesizing facial geometry at medium and fine scales from diffusely-lit facial texture maps.  When applied to an image sequence, the synthesized detail is temporally coherent.  Unlike current state-of-the-art methods, which assume "dark is deep", our model is trained with measured facial detail collected using polarized gradient illumination in a Light Stage. This enables us to produce plausible facial detail across the entire face, including where previous approaches may incorrectly interpret dark features as concavities such as at moles, hair stubble, and occluded pores. Instead of directly inferring 3D geometry, we propose to encode fine details in high-resolution displacement maps which are learned through a hybrid network adopting the state-of-the-art image-to-image translation network and super resolution network. To effectively capture geometric detail at both mid- and high frequencies, we factorize the learning into two separate sub-networks, enabling the full range of facial detail to be modeled.  Results from our learning-based approach compare favorably with a high-quality active facial scanning technique, and require only a single passive lighting condition without a complex scanning setup.
Hand PointNet: 3D Hand Pose Estimation Using Point Sets
Authors: Liuhao Ge, Yujun Cai, Junwu Weng, Junsong Yuan
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018 (Main Conference Track), 2018

Abstract: Convolutional Neural Network (CNN) has shown promising results for 3D hand pose estimation in depth images. Different from existing CNN-based hand pose estimation methods that take either 2D images or 3D volumes as the input, our proposed Hand PointNet directly processes the 3D point cloud that models the visible surface of the hand for pose regression. Taking the normalized point cloud as the input, our proposed hand pose regression network is able to capture complex hand structures and accurately regress a low dimensional representation of the 3D hand pose. In order to further improve the accuracy of fingertips, we design a fingertip refinement network that directly takes the neighboring points of the estimated fingertip location as input to refine the fingertip location. Experiments on three challenging hand pose datasets show that our proposed method outperforms state-of-the-art methods.
Seeing Voices and Hearing Faces: Cross-Modal Biometric Matching
Authors: Arsha Nagrani, Samuel Albanie, Andrew Zisserman
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018 (Main Conference Track), 2018

Abstract: We introduce a seemingly impossible task: given only an audio clip of someone speaking, decide which of two face images is the speaker. In this paper we study this, and a number of related cross-modal tasks, aimed at answering the question: how much can we infer from the voice about the face and vice versa? We study this task “in the wild”, employing the datasets that are now publicly available for face recognition from static images (VGGFace) and speaker identification from audio (VoxCeleb). These provide training and testing scenarios for both static and dynamic testing of cross-modal matching. We make the following contributions: (i) we introduce CNN architectures for both binary and multi-way cross-modal face and audio matching; (ii) we compare dynamic testing (where video information is available, but the audio is not from the same video) with static testing (where only a single still image is available); and (iii) we use hu- man testing as a baseline to calibrate the difficulty of the task. We show that a CNN can indeed be trained to solve this task in both the static and dynamic scenarios, and is even well above chance on 10-way classification of the face given the voice. The CNN matches human performance on easy examples (e.g. different gender across faces) but exceeds human performance on more challenging examples (e.g. faces with the same gender, age and nationality).
Learning Monocular 3D Human Pose Estimation From Multi-View Images
Authors: Helge Rhodin, Jörg Spörri, Isinsu Katircioglu, Victor Constantin, Frédéric Meyer, Erich Müller, Mathieu Salzmann, Pascal Fua
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018 (Main Conference Track), 2018

Abstract: Accurate 3D human pose estimation from single images is possible with sophisticated deep-net architectures that have been trained on very large datasets. However, this still leaves open the problem of capturing motions for which no such database exists.  Manual annotation is tedious, slow, and error-prone. In this paper, we propose to replace most of the annotations by the use of multiple views, at training time only. Specifically, we train the system to predict the same pose in all views. Such a consistency constraint is necessary but not sufficient to predict accurate poses. We therefore complement it with a supervised loss aiming to predict the correct pose in a small set of labeled images, and with a regularization term that penalizes drift from initial predictions. Furthermore, we propose a method to estimate camera pose jointly with human pose, which lets us utilize multi-view footage where calibration is difficult, e.g., for pan-tilt or moving handheld cameras. We demonstrate the effectiveness of our approach on established benchmarks, as well as on a new Ski dataset with rotating cameras and expert ski motion, for which annotations are truly hard to obtain.
Separating Style and Content for Generalized Style Transfer
Authors: Yexun Zhang, Ya Zhang, Wenbin Cai
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018 (Main Conference Track), 2018

Abstract: Neural style transfer has drawn broad attention in recent years. However, most existing methods aim to explicitly model the transformation between different styles, and the learned model is thus not generalizable to new styles. We here attempt to separate the representations for styles and contents, and propose a generalized style transfer network consisting of style encoder, content encoder, mixer and decoder. The style encoder and content encoder are used to extract the style and content factors from the style reference images and content reference images, respectively. The mixer employs a bilinear model to integrate the above two factors and finally feeds it into a decoder to generate images with target style and content. To separate the style features and content features, we leverage the conditional dependence of styles and contents given an image. During training, the encoder network learns to extract styles and contents from two sets of reference images in limited size, one with shared style and the other with shared content. This learning framework allows simultaneous style transfer among multiple styles and can be deemed as a special `multi-task' learning scenario. The encoders are expected to capture the underlying features for different styles and contents which is generalizable to new styles and contents. For validation, we applied the proposed algorithm to the Chinese Typeface transfer problem. Extensive experiment results on character generation have demonstrated the effectiveness and robustness of our method.
TextureGAN: Controlling Deep Image Synthesis With Texture Patches
Authors: Wenqi Xian, Patsorn Sangkloy, Varun Agrawal, Amit Raj, Jingwan Lu, Chen Fang, Fisher Yu, James Hays
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018 (Main Conference Track), 2018

Abstract: In this paper, we investigate deep image synthesis guided by sketch, color, and texture. Previous image synthesis methods can be controlled by sketch and color strokes but we are the first to examine texture control. We allow a user to place a texture patch on a sketch at arbitrary locations and scales to control the desired output texture.  Our generative network learns to synthesize objects consistent with these texture suggestions. To achieve this, we develop a local texture loss in addition to adversarial and content loss to train the generative network. We conduct experiments using sketches generated from real images and textures sampled from a separate texture database and results show that our proposed algorithm is able to generate plausible images that are faithful to user controls. Ablation studies show that our proposed pipeline can generate more realistic images than adapting existing methods directly.
Connecting Pixels to Privacy and Utility: Automatic Redaction of Private Information in Images
Authors: Tribhuvanesh Orekondy, Mario Fritz, Bernt Schiele
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018 (Main Conference Track), 2018

Abstract: Images convey a broad spectrum of personal information.  If such images are shared on social media platforms, this personal information is leaked which conflicts with the privacy of depicted persons. Therefore, we aim for automated approaches to redact such private information and thereby protect privacy of the individual.  By conducting a user study we find that obfuscating the image regions related to the private information leads to privacy while retaining utility of the images. Moreover, by varying the size of the regions different privacy-utility trade-offs can be achieved.  Our findings argue for a "redaction by segmentation" paradigm.   Hence, we propose the first sizable dataset of private images "in the wild" annotated with pixel and instance level labels across a broad range of privacy classes.  We present the first model for automatic redaction of diverse private information.  It is effective at achieving various privacy-utility trade-offs within 83% of the performance of redactions based on ground-truth annotation.
MapNet: An Allocentric Spatial Memory for Mapping Environments
Authors: João F. Henriques, Andrea Vedaldi
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018 (Main Conference Track), 2018

Abstract: Autonomous agents need to reason about the world beyond their instantaneous sensory input. Integrating information over time, however, requires switching from an egocentric representation of a scene to an allocentric one, expressed in the world reference frame. It must also be possible to update the representation dynamically, which requires localizing and registering the sensor with respect to it. In this paper, we develop a differentiable module that satisfies such requirements, while being robust, efficient, and suitable for integration in end-to-end deep networks. The module contains an allocentric spatial memory that can be accessed associatively by feeding to it the current sensory input, resulting in localization, and then updated using an LSTM or similar mechanism. We formulate efficient localization and registration of sensory information as a dual pair of convolution/deconvolution operators in memory space. The map itself is a 2.5D representation of an environment storing information that a deep neural network module learns to distill from RGBD input. The result is a map that contains multi-task information, different from classical approaches to mapping such as structure-from-motion. We present results using synthetic mazes, a dataset of hours of recorded gameplay of the classic game Doom, and the very recent Active Vision Dataset of real images captured from a robot.
Accurate and Diverse Sampling of Sequences Based on a “Best of Many” Sample Objective
Authors: Apratim Bhattacharyya, Bernt Schiele, Mario Fritz
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018 (Main Conference Track), 2018

Abstract: For autonomous agents to successfully operate in the real world, anticipation of future events and states of their environment is a key competence. This problem has been formalized as a sequence extrapolation problem, where a number of observations are used to predict the sequence into the future. Real-world scenarios demand a model of uncertainty of such predictions, as predictions become increasingly uncertain -- in particular on long time horizons. While impressive results have been shown on point estimates, scenarios that induce multi-modal distributions over future sequences remain challenging. Our work addresses these challenges in a Gaussian Latent Variable model for sequence prediction. Our core contribution is a ``Best of Many'' sample objective that leads to more accurate and more diverse predictions that better capture the true variations in real-world sequence data. Beyond our analysis of improved model fit, our models also empirically outperform prior work on three diverse tasks ranging from traffic scenes to weather data.
VirtualHome: Simulating Household Activities via Programs
Authors: Xavier Puig, Kevin Ra, Marko Boben, Jiaman Li, Tingwu Wang, Sanja Fidler, Antonio Torralba
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018 (Main Conference Track), 2018

Abstract: In this paper, we are interested in modeling complex activities that occur in a typical household. We propose to use programs, i.e., sequences of atomic actions and interactions, as a high level representation of complex tasks. Programs are interesting because they provide a non-ambiguous representation of a task, and allow agents to execute them. However, nowadays, there is no database providing this type of information. Towards this goal, we first crowd-source programs for a variety of activities that happen in people's homes, via a game-like interface used for teaching kids how to code. Using the collected dataset, we show how we can learn to extract programs directly from natural language descriptions or from videos.  We then implement the most common atomic (inter)actions in the Unity3D game engine, and use our programs to "drive'' an artificial agent to execute tasks in a simulated household environment. Our VirtualHome simulator allows us to create a large activity video dataset with rich ground-truth, enabling training and testing of video understanding models. We further showcase examples of our agent performing tasks in our VirtualHome based on language
Generate to Adapt: Aligning Domains Using Generative Adversarial Networks
Authors: Swami Sankaranarayanan, Yogesh Balaji, Carlos D. Castillo, Rama Chellappa
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018 (Main Conference Track), 2018

Abstract: Domain Adaptation is an actively researched problem in Computer Vision. In this work, we propose an approach that leverages unsupervised data to bring the source and target distributions closer in a learned joint feature space. We accomplish this by inducing a symbiotic relationship between the learned embedding and a generative adversarial network. This is in contrast to methods which use the adversarial framework for realistic data generation and retraining deep models with such data. We demonstrate the strength and generality of our approach by performing experiments on three different tasks with varying levels of difficulty: (1) Digit classification (MNIST, SVHN and USPS datasets) (2) Object recognition using OFFICE dataset and (3) Domain adaptation from synthetic to real data. Our method achieves state-of-the art performance in most experimental settings and by far the only GAN-based method that has been shown to work well across different datasets such as OFFICE and DIGITS.
Multi-Agent Diverse Generative Adversarial Networks
Authors: Arnab Ghosh, Viveka Kulharia, Vinay P. Namboodiri, Philip H.S. Torr, Puneet K. Dokania
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018 (Main Conference Track), 2018

Abstract: We propose MAD-GAN, an intuitive generalization to the Generative Adversarial Networks (GANs) and its conditional variants to address the well known problem of mode collapse. First, MAD-GAN is a multi-agent GAN architecture incorporating multiple generators and one discriminator. Second, to enforce that different generators capture diverse high probability modes, the discriminator of MAD-GAN is designed such that along with finding the real and fake samples, it is also required to identify the generator that generated the given fake sample. Intuitively, to succeed in this task, the discriminator must learn to push different generators towards different identifiable modes. We perform extensive experiments on synthetic and real datasets and compare MAD-GAN with different variants of GAN. We show high quality diverse sample generations for challenging tasks such as image-to-image translation and face generation. In addition, we also show that MAD-GAN is able to disentangle different modalities when trained using highly challenging diverse-class dataset (e.g. dataset with images of forests, icebergs, and bedrooms). In the end, we show its efficacy on the unsupervised feature representation task.
A PID Controller Approach for Stochastic Optimization of Deep Networks
Authors: Wangpeng An, Haoqian Wang, Qingyun Sun, Jun Xu, Qionghai Dai, Lei Zhang
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018 (Main Conference Track), 2018

Abstract: Deep neural networks have demonstrated their power in many computer vision applications. State-of-the-art deep architectures such as VGG, ResNet, and DenseNet are mostly optimized by the SGD-Momentum algorithm, which updates the weights by considering their past and current gradients. Nonetheless, SGD-Momentum suffers from the overshoot problem, which hinders the convergence of network training. Inspired by the prominent success of proportional-integral-derivative (PID) controller in automatic control, we propose a PID approach for accelerating deep network optimization. We first reveal the intrinsic connections between SGD-Momentum and PID based controller, then present the optimization algorithm which exploits the past, current, and change of gradients to update the network parameters. The proposed PID method reduces much the overshoot phenomena of SGD-Momentum, and it achieves up to 50% acceleration on popular deep network architectures with competitive accuracy, as verified by our experiments on the benchmark datasets including CIFAR10, CIFAR100, and Tiny-ImageNet.
“Learning-Compression” Algorithms for Neural Net Pruning
Authors: Miguel Á. Carreira-Perpiñán, Yerlan Idelbayev
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018 (Main Conference Track), 2018

Abstract: Pruning a neural net consists of removing weights without degrading its performance. This is an old problem of renewed interest because of the need to compress ever larger nets so they can run in mobile devices. Pruning has been traditionally done by ranking or penalizing weights according to some criterion (such as magnitude), removing low-ranked weights and retraining the remaining ones. We formulate pruning as an optimization problem of finding the weights that minimize the loss while satisfying a pruning cost condition. We give a generic algorithm to solve this which alternates "learning" steps that optimize a regularized, data-dependent loss and "compression" steps that mark weights for pruning in a data-independent way. Magnitude thresholding arises naturally in the compression step, but unlike existing magnitude pruning approaches, our algorithm explores subsets of weights rather than committing irrevocably to a specific subset from the beginning. It is also able to learn automatically the best number of weights to prune in each layer of the net without incurring an exponentially costly model selection. Using a single pruning-level user parameter, we achieve state-of-the-art pruning in nets of various sizes.
Large-Scale Distance Metric Learning With Uncertainty
Authors: Qi Qian, Jiasheng Tang, Hao Li, Shenghuo Zhu, Rong Jin
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018 (Main Conference Track), 2018

Abstract: Distance metric learning (DML) has been studied extensively in the past decades for its superior performance with distance-based algorithms. Most of the existing methods propose to learn a distance metric with pairwise or triplet constraints. However, the number of constraints is quadratic or even cubic in the number of the original examples, which makes it challenging for DML to handle the large-scale data set. Besides, the real-world data may contain various uncertainty, especially for the image data. The uncertainty can mislead the learning procedure and cause the performance degradation. By investigating the image data, we find that the original data can be observed from a small set of clean latent examples with different distortions. In this work, we propose the margin preserving metric learning framework to learn the distance metric and latent examples simultaneously. By leveraging the ideal properties of latent examples, the training efficiency can be improved significantly while the learned metric also becomes robust to the uncertainty in the original data. Furthermore, we can show that the metric is learned from latent examples only, but it can preserve the large margin property even for the original data. The empirical study on the benchmark image data sets demonstrates the efficacy and efficiency of the proposed method.
Guide Me: Interacting With Deep Networks
Authors: Christian Rupprecht, Iro Laina, Nassir Navab, Gregory D. Hager, Federico Tombari
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018 (Main Conference Track), 2018

Abstract: Interaction and collaboration between humans and intelligent machines has become increasingly important as machine learning methods move into real-world applications that involve end users. While much prior work lies at the intersection of natural language and vision, such as image captioning or image generation from text descriptions, less focus has been placed on the use of language to guide or improve the performance of a learned visual processing algorithm. In this paper, we explore methods to flexibly guide a trained convolutional neural network through user input to improve its performance during inference. We do so by inserting a layer that acts as a spatio-semantic guide into the network. This guide is trained to modify the network's activations, either directly via an energy minimization scheme or indirectly through a recurrent model that translates human language queries to interaction weights. Learning the verbal interaction is fully automatic and does not require manual text annotations. We evaluate the method on two datasets, showing that guiding a pre-trained network can improve performance, and provide extensive insights into the interaction between the guide and the CNN.
Art of Singular Vectors and Universal Adversarial Perturbations
Authors: Valentin Khrulkov, Ivan Oseledets
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018 (Main Conference Track), 2018

Abstract: Vulnerability of Deep Neural Networks (DNNs) to adversarial attacks has been attracting a lot of attention in recent studies. It has been shown that for many state of the art DNNs performing image classification there exist universal adversarial perturbations --- image-agnostic perturbations mere addition of which to natural images with high probability leads to their misclassification. In this work we propose a new algorithm for constructing such universal perturbations. Our approach is based on computing the so-called (p, q)-singular vectors of the Jacobian matrices of hidden layers of a network. Resulting perturbations present interesting visual patterns, and by using only 64 images we were able to construct universal perturbations with more than 60 % fooling rate on the dataset consisting of 50000 images. We also investigate a correlation between the maximal singular value of the Jacobian matrix and the fooling rate of the corresponding singular vector, and show that the constructed perturbations generalize across networks.
Deflecting Adversarial Attacks With Pixel Deflection
Authors: Aaditya Prakash, Nick Moran, Solomon Garber, Antonella DiLillo, James Storer
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018 (Main Conference Track), 2018

Abstract: CNNs are poised to become integral parts of many critical systems. Despite their robustness to natural variations, image pixel values can be manipulated, via small, carefully crafted, imperceptible perturbations, to cause a model to misclassify images. We present an algorithm to process an image so that classification accuracy is significantly preserved in the presence of such adversarial manipulations. Image classifiers tend to be robust to natural noise, and adversarial attacks tend to be agnostic to object location. These observations motivate our strategy, which leverages model robustness to defend against adversarial perturbations by forcing the image to match natural image statistics. Our algorithm locally corrupts the image by redistributing pixel values via a process we term pixel deflection. A subsequent wavelet-based denoising operation softens this corruption, as well as some of the adversarial changes. We demonstrate experimentally that the combination of these techniques enables the effective recovery of the true class, against a variety of robust attacks. Our results compare favorably with current state-of-the-art defenses, without requiring retraining or modifying the CNN.
MovieGraphs: Towards Understanding Human-Centric Situations From Videos
Authors: Paul Vicol, Makarand Tapaswi, Lluís Castrejón, Sanja Fidler
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018 (Main Conference Track), 2018

Abstract: There is growing interest in artificial intelligence to build socially intelligent robots. This requires machines to have the ability to "read" people's emotions, motivations, and other factors that affect behavior. Towards this goal, we introduce a novel dataset called MovieGraphs which provides detailed, graph-based annotations of social situations depicted in movie clips. Each graph consists of several types of nodes, to capture who is present in the clip, their emotional and physical attributes, their relationships (i.e., parent/child), and the interactions between them. Most interactions are associated with topics that provide additional details, and reasons that give motivations for actions. In addition, most interactions and many attributes are grounded in the video with time stamps. We provide a thorough analysis of our dataset, showing interesting common-sense correlations between different social aspects of scenes, as well as across scenes over time. We propose a method for querying videos and text with graphs, and show that: 1) our graphs contain rich and sufficient information to summarize and localize each scene; and 2) subgraphs allow us to describe situations at an abstract level and retrieve multiple semantically relevant situations. We also propose methods for interaction understanding via ordering, and reason understanding. MovieGraphs is the first benchmark to focus on inferred properties of human-centric situations, and opens up an exciting avenue towards socially-intelligent AI agents.
SemStyle: Learning to Generate Stylised Image Captions Using Unaligned Text
Authors: Alexander Mathews, Lexing Xie, Xuming He
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018 (Main Conference Track), 2018

Abstract: Linguistic style is an essential part of written communication, with the power to affect both clarity and attractiveness. With recent advances in vision and language, we can start to tackle the problem of generating image captions that are both visually grounded and appropriately styled. Existing approaches either require styled training captions aligned to images or generate captions with low relevance. We develop a model that learns to generate visually relevant styled captions from a large corpus of styled text without aligned images. The core idea of this model, called SemStyle, is to separate semantics and style. One key component is a novel and concise semantic term representation generated using natural language processing techniques and frame semantics. In addition, we develop a unified language model that decodes sentences with diverse word choices and syntax for different styles. Evaluations, both automatic and manual, show captions from SemStyle preserve image semantics, are descriptive, and are style shifted. More broadly, this work provides possibilities to learn richer image descriptions from the plethora of linguistic data available on the web.
Benchmarking 6DOF Outdoor Visual Localization in Changing Conditions
Authors: Torsten Sattler, Will Maddern, Carl Toft, Akihiko Torii, Lars Hammarstrand, Erik Stenborg, Daniel Safari, Masatoshi Okutomi, Marc Pollefeys, Josef Sivic, Fredrik Kahl, Tomas Pajdla
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018 (Main Conference Track), 2018

Abstract: Visual localization enables autonomous vehicles to navigate in their surroundings and augmented reality applications to link virtual to real worlds. Practical visual localization approaches need to be robust to a wide variety of viewing condition, including day-night changes, as well as weather and seasonal variations, while providing highly accurate 6 degree-of-freedom (6DOF) camera pose estimates. In this paper, we introduce the first benchmark datasets specifically designed for analyzing the impact of such factors on visual localization. Using carefully created ground truth poses for query images taken under a wide variety of conditions, we evaluate the impact of various factors on 6DOF camera pose estimation accuracy through extensive experiments with state-of-the-art localization approaches. Based on our results, we draw conclusions about the difficulty of different conditions, showing that long-term localization is far from solved, and  propose promising avenues for future work, including sequence-based localization approaches and the need for better local features. Our benchmark is available at visuallocalization.net.
IVQA: Inverse Visual Question Answering
Authors: Feng Liu, Tao Xiang, Timothy M. Hospedales, Wankou Yang, Changyin Sun
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018 (Main Conference Track), 2018

Abstract: We propose the inverse problem of Visual question answering (iVQA), and explore its suitability as a benchmark for visuo-linguistic understanding. The iVQA task is to generate a question that corresponds to a given image and answer pair. Since the answers are less informative than the questions, and the questions have less learnable bias, an iVQA model needs to better understand the image to be successful than a VQA model. We pose question generation as a multi-modal dynamic inference process and propose an iVQA model that can gradually adjust its focus of attention guided by both a partially generated question and the answer. For evaluation, apart from existing linguistic metrics, we propose  a new ranking metric. This metric compares the ground truth question's rank among a list of distractors, which allows the drawbacks of different algorithms and sources of error to be studied. Experimental results show that our model can generate diverse,  grammatically correct and content correlated questions that match the given answer.
Unsupervised Person Image Synthesis in Arbitrary Poses
Authors: Albert Pumarola, Antonio Agudo, Alberto Sanfeliu, Francesc Moreno-Noguer
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018 (Main Conference Track), 2018

Abstract: We present a novel approach for synthesizing photo-realistic images of people in arbitrary poses using generative adversarial learning. Given an input image of a person and a desired pose represented by a 2D skeleton, our model renders the image of the same person under the new pose, synthesizing novel views of the parts visible in the input image and hallucinating those that are not seen. This problem has recently been addressed in a supervised manner, i.e., during training the ground truth images under the new poses are given  to the network. We go beyond these approaches by proposing a fully unsupervised strategy. We tackle this challenging scenario by splitting the problem into two principal subtasks.  First, we consider a pose conditioned bidirectional generator that maps back the initially rendered image to the original pose, hence being directly comparable to the input image without the need to resort to any training image. Second, we devise a novel loss function that incorporates content and style terms, and aims at producing images of high perceptual quality. Extensive experiments conducted on the DeepFashion dataset demonstrate that the images rendered by our model are very close in appearance to those obtained by fully supervised approaches.
Learning Descriptor Networks for 3D Shape Synthesis and Analysis
Authors: Jianwen Xie, Zilong Zheng, Ruiqi Gao, Wenguan Wang, Song-Chun Zhu, Ying Nian Wu
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018 (Main Conference Track), 2018

Abstract: This paper proposes a 3D shape descriptor network, which is a deep convolutional energy-based model, for  modeling volumetric shape patterns. The maximum likelihood training of the model follows an "analysis by synthesis" scheme and can be interpreted as a mode seeking and mode shifting process. The model can synthesize 3D shape patterns by sampling  from the probability distribution via MCMC such as Langevin dynamics. The model can be used to train a 3D generator network via MCMC teaching. The conditional version of the 3D shape descriptor net can be used for 3D object recovery and 3D object super-resolution. Experiments demonstrate that the proposed model can generate realistic 3D shape patterns and can be useful for 3D shape analysis.
Neural Kinematic Networks for Unsupervised Motion Retargetting
Authors: Ruben Villegas, Jimei Yang, Duygu Ceylan, Honglak Lee
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018 (Main Conference Track), 2018

Abstract: We propose a recurrent neural network architecture with a Forward Kinematics layer and cycle consistency based adversarial training objective for unsupervised motion retargetting. Our network captures the high-level properties of an input motion by the forward kinematics layer, and adapts them to a target character with different skeleton bone lengths (e.g., shorter, longer arms etc.). Collecting paired motion training sequences from different characters is expensive. Instead, our network utilizes cycle consistency to learn to solve the Inverse Kinematics problem in an unsupervised manner. Our method works online, i.e., it adapts the motion sequence on-the-fly as new frames are received. In our experiments, we use the Mixamo animation data to test our method for a variety of motions and characters and achieve state-of-the-art results. We also demonstrate motion retargetting from monocular human videos to 3D characters using an off-the-shelf 3D pose estimator.
Group Consistent Similarity Learning via Deep CRF for Person Re-Identification
Authors: Dapeng Chen, Dan Xu, Hongsheng Li, Nicu Sebe, Xiaogang Wang
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018 (Main Conference Track), 2018

Abstract: Person re-identification benefits greatly from deep neural networks (DNN) to learn accurate similarity metrics and robust feature embeddings. However, most of the current methods impose only local constraints for similarity learning. In this paper, we incorporate constraints on large image groups by combining the CRF with deep neural networks. The proposed method aims to learn the ``local similarity" metrics for image pairs while taking into account the dependencies from all the images in a group, forming ``group similarities". Our method involves multiple images to model the relationships among the local and global similarities in a unified CRF during training, while combines multi-scale local similarities as the predicted similarity in testing. We adopt an approximate inference scheme for estimating the group similarity, enabling end-to-end training. Extensive experiments demonstrate the effectiveness of our model that combines DNN and CRF for learning robust multi-scale local similarities. The overall results outperform those by state-of-the-arts with considerable margins on three widely-used benchmarks.
Learning Compositional Visual Concepts With Mutual Consistency
Authors: Yunye Gong, Srikrishna Karanam, Ziyan Wu, Kuan-Chuan Peng, Jan Ernst, Peter C. Doerschuk
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018 (Main Conference Track), 2018

Abstract: Compositionality of semantic concepts in image synthesis and analysis is appealing as it can help in decomposing known and generatively recomposing unknown data. For instance, we may learn concepts of changing illumination, geometry or albedo of a scene, and try to recombine them to generate physically meaningful, but unseen data for training and testing. In practice however we often do not have samples from the joint concept space available: We may have data on illumination change in one data set and on geometric change in another one without complete overlap. We pose the following question: How can we learn two or more concepts jointly from different data sets with mutual consistency where we do not have samples from the full joint space? We present a novel answer in this paper based on cyclic consistency over multiple concepts, represented individually by generative adversarial networks (GANs). Our method, ConceptGAN, can be understood as a drop in for data augmentation to improve resilience for real world applications. Qualitative and quantitative evaluations demonstrate its efficacy in generating semantically meaningful images, as well as one shot face verification as an example application.
NestedNet: Learning Nested Sparse Structures in Deep Neural Networks
Authors: Eunwoo Kim, Chanho Ahn, Songhwai Oh
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018 (Main Conference Track), 2018

Abstract: Recently, there have been increasing demands to construct compact deep architectures to remove unnecessary redundancy and to improve the inference speed. While many recent works focus on reducing the redundancy by eliminating unneeded weight parameters, it is not possible to apply a single deep network for multiple devices with different resources. When a new device or circumstantial condition requires a new deep architecture, it is necessary to construct and train a new network from scratch. In this work, we propose a novel deep learning framework, called a nested sparse network, which exploits an n-in-1-type nested structure in a neural network. A nested sparse network consists of multiple levels of networks with a different sparsity ratio associated with each level, and higher level networks share parameters with lower level networks to enable stable nested learning. The proposed framework realizes a resource-aware versatile architecture as the same network can meet diverse resource requirements, i.e., anytime property. Moreover, the proposed nested network can learn different forms of knowledge in its internal networks at different levels, enabling multiple tasks using a single  network, such as coarse-to-fine hierarchical classification. In order to train the proposed nested network, we propose efficient weight connection learning and channel and layer scheduling strategies. We evaluate our network in multiple tasks, including adaptive deep compression, knowledge distillation, and learning class hierarchy, and demonstrate that nested sparse networks perform competitively, but more efficiently, compared to existing methods.