mtybilly/Instruct2DS huggingface dataset:
Fooling Vision and Language Models Despite Localization and Attention Mechanism
Authors: Xinyun Chen, Chang Liu, Anna Rohrbach, Trevor Darrell, Dawn Song Xiaojun Xu
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018 (Main Conference Track), 2018

Abstract: Adversarial attacks are known to succeed on classifiers, but it has been an open question whether more complex vision systems are vulnerable. In this paper, we study adversarial examples for vision and language models, which incorporate natural language understanding and complex structures such as attention, localization, and modular architectures. In particular, we investigate attacks on a dense captioning model and on two visual question answering (VQA) models. Our evaluation shows that we can generate adversarial examples with a high success rate (i.e., >90%) for these models. Our work sheds new light on understanding adversarial attacks on vision systems which have a language component and shows that attention, bounding box localization, and compositional internal structures are vulnerable to adversarial attacks. These observations will inform future work towards building effective defenses.
Categorizing Concepts With Basic Level for Vision-to-Language
Authors: Hanli Wang, Kaisheng Xu Hanzhang Wang
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018 (Main Conference Track), 2018

Abstract: Vision-to-language tasks require a unified semantic understanding of visual content. However, the information contained in image/video is essentially ambiguous on two perspectives manifested on the diverse understanding among different persons and the various understanding grains even for the same person. Inspired by the basic level in early cognition, a Basic Concept (BaC) category is proposed in this work that contains both consensus and proper level of visual content to help neural network tackle the above problems. Specifically, a salient concept category is firstly generated by intersecting the labels of ImageNet and the vocabulary of MSCOCO dataset. Then, according to the observation from human early cognition that children make fewer mistakes on the basic level, the salient category is further refined by clustering concepts with a defined confusion degree which measures the difficulty for convolutional neural network to distinguish class pairs. Finally, a pre-trained model based on GoogLeNet is produced with the proposed BaC category of 1,372 concept classes. To verify the effectiveness of the proposed categorizing method for vision-to-language tasks, two kinds of experiments are performed including image captioning and visual question answering with the benchmark datasets of MSCOCO, Flickr30k and COCO-QA. The experimental results demonstrate that the representations derived from the cognition-inspired BaC category promote representation learning of neural networks on vision-to-language tasks, and a performance improvement is gained without modifying standard models.
Don't Just Assume; Look and Answer: Overcoming Priors for Visual Question Answering
Authors: Dhruv Batra, Devi Parikh, Aniruddha Kembhavi Aishwarya Agrawal
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018 (Main Conference Track), 2018

Abstract: A number of studies have found that today's Visual Question Answering (VQA) models are heavily driven by superficial correlations in the training data and lack sufficient image grounding. To encourage development of models geared towards the latter, we propose a new setting for VQA where for every question type, train and test sets have different prior distributions of answers. Specifically, we present new splits of the VQA v1 and VQA v2 datasets, which we call Visual Question Answering under Changing Priors (VQA-CP v1 and VQA-CP v2 respectively). First, we evaluate several existing VQA models under this new setting and show that their performance degrades significantly compared to the original VQA setting. Second, we propose a novel Grounded Visual Question Answering model (GVQA) that contains inductive biases and restrictions in the architecture specifically designed to prevent the model from 'cheating' by primarily relying on priors in the training data. Specifically, GVQA explicitly disentangles the recognition of visual concepts present in the image from the identification of plausible answer space for a given question, enabling the model to more robustly generalize across different distributions of answers. GVQA is built off an existing VQA model -- Stacked Attention Networks (SAN). Our experiments demonstrate that GVQA significantly outperforms SAN on both VQA-CP v1 and VQA-CP v2 datasets. Interestingly, it also outperforms more powerful VQA models such as Multimodal Compact Bilinear Pooling (MCB) in several cases. GVQA offers strengths complementary to SAN when trained and evaluated on the original VQA v1 and VQA v2 datasets. Finally, GVQA is more transparent and interpretable than existing VQA models.
Learning Pixel-Level Semantic Affinity With Image-Level Supervision for Weakly Supervised Semantic Segmentation
Authors: Suha Kwak Jiwoon Ahn
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018 (Main Conference Track), 2018

Abstract: The deficiency of segmentation labels is one of the main obstacles to semantic segmentation in the wild. To alleviate this issue, we present a novel framework that generates segmentation labels of images given their image-level class labels. In this weakly supervised setting, trained models have been known to segment local discriminative parts rather than the entire object area. Our solution is to propagate such local responses to nearby areas which belong to the same semantic entity. To this end, we propose a Deep Neural Network (DNN) called AffinityNet that predicts semantic affinity between a pair of adjacent image coordinates. The semantic propagation is then realized by random walk with the affinities predicted by AffinityNet. More importantly, the supervision employed to train AffinityNet is given by the initial discriminative part segmentation, which is incomplete as a segmentation annotation but sufficient for learning semantic affinities within small image areas. Thus the entire framework relies only on image-level class labels and does not require any extra data or annotations. On the PASCAL VOC 2012 dataset, a DNN learned with segmentation labels generated by our method outperforms previous models trained with the same level of supervision, and is even as competitive as those relying on stronger supervision.
From Lifestyle Vlogs to Everyday Interactions
Authors: Wei-cheng Kuo, Alexei A. Efros, Jitendra Malik David F. Fouhey
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018 (Main Conference Track), 2018

Abstract: A major stumbling block to progress in understanding basic human interactions, such as getting out of bed or opening a refrigerator, is lack of good training data. Most past efforts have gathered this data explicitly: starting with a laundry list of action labels, and then querying search engines for videos tagged with each label. In this work, we do the reverse and search implicitly: we start with a large collection of interaction-rich video data and then annotate and analyze it.  We use Internet Lifestyle Vlogs as the source of surprisingly large and diverse interaction data.   We show that by collecting the data first, we are able to achieve greater scale and far greater diversity in terms of actions and actors. Additionally, our data exposes biases built into common explicitly gathered data. We make sense of our data by analyzing the central component of interaction -- hands. We benchmark two tasks: identifying semantic object contact at the video level and non-semantic contact state at the frame level. We additionally demonstrate future prediction of hands.
Cross-Domain Weakly-Supervised Object Detection Through Progressive Domain Adaptation
Authors: Ryosuke Furuta, Toshihiko Yamasaki, Kiyoharu Aizawa Naoto Inoue
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018 (Main Conference Track), 2018

Abstract: Can we detect common objects in a variety of image domains without instance-level annotations? In this paper, we present a framework for a novel task, cross-domain weakly supervised object detection, which addresses this question. For this paper, we have access to images with instance-level annotations in a source domain (e.g., natural image) and images with image-level annotations in a target domain (e.g., watercolor). In addition, the classes to be detected in the target domain are all or a subset of those in the source domain. Starting from a fully supervised object detector, which is pre-trained on the source domain, we propose a two-step progressive domain adaptation technique by fine-tuning the detector on two types of artificially and automatically generated samples. We test our methods on our newly collected datasets containing three image domains, and achieve an improvement of approximately 5 to 20 percentage points in terms of mean average precision (mAP) compared to the best-performing baselines.
RotationNet: Joint Object Categorization and Pose Estimation Using Multiviews From Unsupervised Viewpoints
Authors: Yasuyuki Matsushita, Yoshifumi Nishida Asako Kanezaki
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018 (Main Conference Track), 2018

Abstract: We propose a Convolutional Neural Network (CNN)-based model ``RotationNet,'' which takes multi-view images of an object as input and jointly estimates its pose and object category. Unlike previous approaches that use known viewpoint labels for training, our method treats the viewpoint labels as latent variables, which are learned in an unsupervised manner during the training using an unaligned object dataset. RotationNet is designed to use only a partial set of multi-view images for inference, and this property makes it useful in practical scenarios where only partial views are available. Moreover, our pose alignment strategy enables one to obtain view-specific feature representations shared across classes, which is important to maintain high accuracy in both object categorization and pose estimation. Effectiveness of RotationNet is demonstrated by its superior performance to the state-of-the-art methods of 3D object classification on 10- and 40-class ModelNet datasets. We also show that RotationNet, even trained without known poses, achieves the state-of-the-art performance on an object pose estimation dataset.
An End-to-End TextSpotter With Explicit Alignment and Attention
Authors: Zhi Tian, Weilin Huang, Chunhua Shen, Yu Qiao, Changming Sun Tong He
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018 (Main Conference Track), 2018

Abstract: Text detection and recognition in natural images have long been considered as two separate tasks that are processed sequentially. Jointly training two tasks is non-trivial due to significant differences in learning difficulties and convergence rates. In this work, we present a conceptually simple yet efficient framework that simultaneously processes the two tasks in a united framework. Our main contributions are three-fold: (1) we propose a novel textalignment layer that allows it to precisely compute convolutional features of a text instance in arbitrary orientation, which is the key to boost the performance; (2) a character attention mechanism is introduced by using character spatial information as explicit supervision, leading to large improvements in recognition; (3) two technologies, together with a new RNN branch for word recognition, are integrated seamlessly into a single model which is end-to-end trainable. This allows the two tasks to work collaboratively by sharing convolutional features, which is critical to identify challenging text instances. Our model obtains impressive results in end-to-end recognition on the ICDAR 2015, significantly advancing the most recent results, with improvements of F-measure from (0.54, 0.51, 0.47) to (0.82, 0.77, 0.63), by using a strong, weak and generic lexicon respectively. Thanks to joint training, our method can also serve as a good detector by achieving a new state-of-the-art detection performance on related benchmarks. Code is available at https://github. com/tonghe90/textspotter.
WILDTRACK: A Multi-Camera HD Dataset for Dense Unscripted Pedestrian Detection
Authors: Pierre Baqué, Stéphane Bouquet, Andrii Maksai, Cijo Jose, Timur Bagautdinov, Louis Lettry, Pascal Fua, Luc Van Gool, François Fleuret Tatjana Chavdarova
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018 (Main Conference Track), 2018

Abstract: People detection methods are highly sensitive to occlusions between pedestrians, which are extremely frequent in many situations where cameras have to be mounted at a limited height. The reduction of camera prices allows for the generalization of static multi-camera set-ups. Using joint visual information from multiple synchronized cameras gives the opportunity to improve detection performance.  In this paper, we present a new large-scale and high-resolution dataset. It has been captured with seven static cameras in a public open area, and unscripted dense groups of pedestrians standing and walking. Together with the camera frames, we provide an accurate joint (extrinsic and intrinsic) calibration, as well as 7 series of 400 annotated frames for detection at a rate of 2 frames per second. This results in over 40,000 bounding boxes delimiting every person present in the area of interest, for a total of more than 300 individuals.   We provide a series of benchmark results using baseline algorithms published over the recent months for multi-view detection with deep neural networks, and trajectory estimation using a non-Markovian model.
Direct Shape Regression Networks for End-to-End Face Alignment
Authors: Xiantong Zhen, Xianglong Liu, Cheng Deng, Vassilis Athitsos, Heng Huang Xin Miao
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018 (Main Conference Track), 2018

Abstract: Face alignment has been extensively studied in computer vision community due to its fundamental role in facial analysis, but it remains an unsolved problem. The major challenges lie in the highly nonlinear relationship between face images and associated facial shapes, which is coupled by underlying correlation of landmarks. Existing methods mainly rely on cascaded regression, suffering from intrinsic shortcomings, e.g., strong dependency on initialization and failure to exploit landmark correlations. In this paper, we propose the direct shape regression network (DSRN) for end-to-end face alignment by jointly handling the aforementioned challenges in a unified framework. Specifically, by deploying doubly convolutional layer and by using the Fourier feature pooling layer proposed in this paper, DSRN efficiently constructs strong representations to disentangle highly nonlinear relationships between images and shapes; by incorporating a linear layer of low-rank learning, DSRN effectively encodes correlations of landmarks to improve performance. DSRN leverages the strengths of kernels for nonlinear feature extraction and neural networks for structured prediction, and provides the first end-to-end learning architecture for direct face alignment. Its effectiveness and generality are validated by extensive experiments on five benchmark datasets, including AFLW, 300W, CelebA, MAFL, and 300VW. All empirical results demonstrate that DSRN consistently produces high performance and in most cases surpasses state-of-the-art.
Natural and Effective Obfuscation by Head Inpainting
Authors: Liqian Ma, Seong Joon Oh, Luc Van Gool, Bernt Schiele, Mario Fritz Qianru Sun
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018 (Main Conference Track), 2018

Abstract: As more and more personal photos are shared online, being able to obfuscate identities in such photos is becoming a necessity for privacy protection. People have largely resorted to blacking out or blurring head regions, but they result in poor user experience while being surprisingly ineffective against state of the art person recognizers[17]. In this work, we propose a novel head inpainting obfuscation technique. Generating a realistic head inpainting in social media photos is challenging because subjects appear in diverse activities and head orientations. We thus split the task into two sub-tasks: (1) facial landmark generation from image context (e.g. body pose) for seamless hypothesis of sensible head pose, and (2) facial landmark conditioned head inpainting. We verify that our inpainting method generates realistic person images, while achieving superior obfuscation performance against automatic person recognizers.
3D Semantic Trajectory Reconstruction From 3D Pixel Continuum
Authors: Ziwei Li, Hyun Soo Park Jae Shin Yoon
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018 (Main Conference Track), 2018

Abstract: This paper presents a method to reconstruct dense semantic trajectory stream of human interactions in 3D from synchronized multiple videos. The interactions inherently introduce self-occlusion and illumination/appearance/shape changes, resulting in highly fragmented trajectory reconstruction with noisy and coarse semantic labels. Our conjecture is that among many views, there exists a set of views that can confidently recognize the visual semantic label of a 3D trajectory. We introduce a new representation called 3D semantic map---a probability distribution over the semantic labels per trajectory. We construct the 3D semantic map by reasoning about visibility and 2D recognition confidence based on view-pooling, i.e., finding the view that best represents the semantics of the trajectory. Using the 3D semantic map, we precisely infer all trajectory labels jointly by considering the affinity between long range trajectories via estimating their local rigid transformations. This inference quantitatively outperforms the baseline approaches in terms of predictive validity, representation robustness, and affinity effectiveness. We demonstrate that our algorithm can robustly compute the semantic labels of a large scale trajectory set involving real-world human interactions with object, scenes, and people.
Optimizing Filter Size in Convolutional Neural Networks for Facial Action Unit Recognition
Authors: Zibo Meng, Zhiyuan Li, James O'Reilly, Jie Cai, Xiaofeng Wang, Yan Tong Shizhong Han
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018 (Main Conference Track), 2018

Abstract: Recognizing facial action units (AUs) during spontaneous facial displays is a challenging problem. Most recently, Convolutional Neural Networks (CNNs) have shown promise for facial AU recognition, where predefined and fixed convolution filter sizes are employed. In order to achieve the best performance, the optimal filter size is often empirically found by conducting extensive experimental validation. Such a training process suffers from expensive training cost, especially as the network becomes deeper.  This paper proposes a novel Optimized Filter Size CNN (OFS-CNN), where the filter sizes and weights of all convolutional layers are learned simultaneously from the training data along with learning convolution filters. Specifically, the filter size is defined as a continuous variable, which is optimized by minimizing the training loss. Experimental results on two AU-coded spontaneous databases have shown that the proposed OFS-CNN is capable of estimating optimal filter size for varying image resolution and outperforms traditional CNNs with the best filter size obtained by exhaustive search. The OFS-CNN also beats the CNN using multiple filter sizes and more importantly, is much more efficient during testing with the proposed forward-backward propagation algorithm.
V2V-PoseNet: Voxel-to-Voxel Prediction Network for Accurate 3D Hand and Human Pose Estimation From a Single Depth Map
Authors: Ju Yong Chang, Kyoung Mu Lee Gyeongsik Moon
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018 (Main Conference Track), 2018

Abstract: Most of the existing deep learning-based methods for 3D hand and human pose estimation from a single depth map are based on a common framework that takes a 2D depth map and directly regresses the 3D coordinates of keypoints, such as hand or human body joints, via 2D convolutional neural networks (CNNs). The first weakness of this approach is the presence of perspective distortion in the 2D depth map. While the depth map is intrinsically 3D data, many previous methods treat depth maps as 2D images that can distort the shape of the actual object through projection from 3D to 2D space. This compels the network to perform perspective distortion-invariant estimation. The second weakness of the conventional approach is that directly regressing 3D coordinates from a 2D image is a highly non-linear mapping, which causes difficulty in the learning procedure. To overcome these weaknesses, we firstly cast the 3D hand and human pose estimation problem from a single depth map into a voxel-to-voxel prediction that uses a 3D voxelized grid and estimates the per-voxel likelihood for each keypoint. We design our model as a 3D CNN that provides accurate estimates while running in real-time. Our system outperforms previous methods in almost all publicly available 3D hand and human pose estimation datasets and placed first in the HANDS 2017 frame-based 3D hand pose estimation challenge. The code is available in https://github.com/mks0601/V2V-PoseNet_RELEASE.
Ring Loss: Convex Feature Normalization for Face Recognition
Authors: Dipan K. Pal, Marios Savvides Yutong Zheng
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018 (Main Conference Track), 2018

Abstract: We motivate and present Ring loss, a simple and elegant feature normalization approach for deep networks designed to augment standard loss functions such as Softmax. We argue that deep feature normalization is an important aspect of supervised classification problems where we require the model to represent each class in a multi-class problem equally well. The direct approach to feature normalization through the hard normalization operation results in a non-convex formulation. Instead, Ring loss applies soft normalization, where it gradually learns to constrain the norm to the scaled unit circle while preserving convexity leading to more robust features. We apply Ring loss to large-scale face recognition problems and present results on LFW, the challenging protocols of IJB-A Janus, Janus CS3 (a superset of IJB-A Janus), Celebrity Frontal-Profile (CFP) and MegaFace with 1 million distractors. Ring loss outperforms strong baselines, matches state-of-the-art performance on IJB-A Janus and outperforms all other results on the challenging Janus CS3 thereby achieving state-of-the-art. We also outperform strong baselines in handling extremely low resolution face matching.
Adversarially Occluded Samples for Person Re-Identification
Authors: Dangwei Li, Zhang Zhang, Xiaotang Chen, Kaiqi Huang Houjing Huang
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018 (Main Conference Track), 2018

Abstract: Person re-identification (ReID) is the task of retrieving particular persons across different cameras. Despite its great progress in recent years, it is still confronted with challenges like pose variation, occlusion, and similar appearance among different persons. The large gap between training and testing performance with existing models implies the insufficiency of generalization. Considering this fact, we propose to augment the variation of training data by introducing Adversarially Occluded Samples. These special samples are both a) meaningful in that they resemble real-scene occlusions, and b) effective in that they are tough for the original model and thus provide the momentum to jump out of local optimum. We mine these samples based on a trained ReID model and with the help of network visualization techniques. Extensive experiments show that the proposed samples help the model discover new discriminative clues on the body and generalize much better at test time. Our strategy makes significant improvement over strong baselines on three large-scale ReID datasets, Market1501, CUHK03 and DukeMTMC-reID.
Classifier Learning With Prior Probabilities for Facial Action Unit Recognition
Authors: Weiming Dong, Bao-Gang Hu, Qiang Ji Yong Zhang
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018 (Main Conference Track), 2018

Abstract: Facial action units (AUs) play an important role in human emotion understanding. One big challenge for data-driven AU recognition approaches is the lack of enough AU annotations, since AU annotation requires strong domain expertise. To alleviate this issue, we propose a knowledge-driven method for jointly learning multiple AU classifiers without any AU annotation by leveraging prior probabilities on AUs, including expression-independent and expression-dependent AU probabilities. These prior probabilities are drawn from facial anatomy and emotion studies, and are independent of datasets. We incorporate the prior probabilities on AUs as the constraints into the objective function of multiple AU classifiers, and develop an efficient learning algorithm to solve the formulated problem. Experimental results on five benchmark expression databases demonstrate the effectiveness of the proposed method, especially its generalization ability, and the power of the prior probabilities.
4DFAB: A Large Scale 4D Database for Facial Expression Analysis and Biometric Applications
Authors: Irene Kotsia, Maja Pantic, Stefanos Zafeiriou Shiyang Cheng
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018 (Main Conference Track), 2018

Abstract: The progress we are currently witnessing in many computer vision applications, including automatic face analysis, would not be made possible without tremendous efforts in collecting and annotating large scale visual databases. To this end, we propose 4DFAB, a new large scale database of dynamic high resolution 3D faces (over 1,800,000 3D meshes). 4DFAB contain recordings of 180 subjects captured in four different sessions spanned over a five-year period. It contains 4D videos of subjects displaying both spontaneous and posed facial behaviours. The database can be used for both face and facial expression recognition, as well as behavioural biometrics. It can also be used to learn very powerful blendshapes for parametrising facial behaviour. In this paper, we conduct several experiments and demonstrate the usefulness of the database in various applications. The database will be made publicly available for research purposes.
Seeing Small Faces From Robust Anchor's Perspective
Authors: Ran Tao, Khoa Luu, Marios Savvides Chenchen Zhu
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018 (Main Conference Track), 2018

Abstract: This paper introduces a novel anchor design principle to support anchor-based face detection for superior scale-invariant performance, especially on tiny faces. To achieve this, we explicitly address the problem that anchor-based detectors drop performance drastically on faces with tiny sizes, e.g. less than 16x16 pixels. In this paper, we investigate why this is the case. We discover that current anchor design cannot guarantee high overlaps between tiny faces and anchor boxes, which increases the difficulty of training. The new Expected Max Overlapping (EMO) score is proposed which can theoretically explain the low overlapping issue and inspire several effective strategies of new anchor design leading to higher face overlaps, including anchor stride reduction with new network architectures, extra shifted anchors, and stochastic face shifting. Comprehensive experiments show that our proposed method significantly outperforms the baseline anchor-based detector, while consistently achieving state-of-the-art results on challenging face detection datasets with competitive runtime speed.
2D/3D Pose Estimation and Action Recognition Using Multitask Deep Learning
Authors: David Picard, Hedi Tabia Diogo C. Luvizon
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018 (Main Conference Track), 2018

Abstract: Action recognition and human pose estimation are closely related but both problems are generally handled as distinct tasks in the literature. In this work, we propose a multitask framework for jointly 2D and 3D pose estimation from still images and human action recognition from video sequences. We show that a single architecture can be used to solve the two problems in an efficient way and still achieves state-of-the-art results. Additionally, we demonstrate that optimization from end-to-end leads to significantly higher accuracy than separated learning. The proposed architecture can be trained with data from different categories simultaneously in a seamlessly way. The reported results on four datasets (MPII, Human3.6M, Penn Action and NTU) demonstrate the effectiveness of our method on the targeted tasks.
Dense 3D Regression for Hand Pose Estimation
Authors: Thomas Probst, Luc Van Gool, Angela Yao Chengde Wan
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018 (Main Conference Track), 2018

Abstract: We present a simple and effective method for 3D hand pose estimation from a single depth frame. As opposed to previous state-of-arts based on holistic 3D regression, our method works on dense pixel-wise estimation. This is achieved by careful design choices in pose parameterization, which leverages both 2D and 3D properties of depth map. Specifically, we decompose the pose parameters into a set of per-pixel estimations, i.e., 2D heat maps, 3D heat maps and unit 3D direction vector fields. The 2D/3D joint heat maps and 3D joint offsets are estimated via multi-task network cascades, which is trained end-to-end. The pixel-wise estimations can be directly translated into a vote casting scheme. A variant of mean shift is then used to aggregate local votes and explicitly handles the global 3D estimation in consensus with pixel-wise 2D and 3D estimations. Our method is efficient and highly accurate. On MSRA and NYU hand dataset, our method outperforms all previous state-of-arts by a large margin. On ICVL hand dataset, our method achieves similar accuracy compared to the state-of-art which is nearly saturated and outperforms other state-of-arts. Code will be made available.
Camera Style Adaptation for Person Re-Identification
Authors: Liang Zheng, Zhedong Zheng, Shaozi Li, Yi Yang Zhun Zhong
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018 (Main Conference Track), 2018

Abstract: Being a cross-camera retrieval task, person re-identification suffers from image style variations caused by different cameras. The art implicitly addresses this problem by learning a camera-invariant descriptor subspace. In this paper, we explicitly consider this challenge by introducing camera style (CamStyle) adaptation. CamStyle can serve as a data augmentation approach that smooths the camera style disparities. Specifically, with CycleGAN, labeled training images can be style-transferred to each camera, and, along with the original training samples, form the augmented training set. This method, while increasing data diversity against over-fitting, also incurs a considerable level of noise. In the effort to alleviate the impact of noise, the label smooth regularization (LSR) is adopted. The vanilla version of our method (without LSR) performs reasonably well on few-camera systems in which over-fitting often occurs. With LSR, we demonstrate consistent improvement in all systems regardless of the extent of over-fitting. We also report competitive accuracy compared with the state of the art.
PoseTrack: A Benchmark for Human Pose Estimation and Tracking
Authors: Umar Iqbal, Eldar Insafutdinov, Leonid Pishchulin, Anton Milan, Juergen Gall, Bernt Schiele Mykhaylo Andriluka
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018 (Main Conference Track), 2018

Abstract: Existing systems for video-based pose estimation and tracking struggle to perform well on realistic videos with multiple people and often fail to output body-pose trajectories consistent over time. To address this shortcoming this paper introduces PoseTrack which is a new large-scale benchmark for video-based human pose estimation and articulated tracking. Our new benchmark encompasses three tasks focusing on i) single-frame multi-person pose estimation, ii) multi-person pose estimation in videos, and iii) multi-person articulated tracking. To establish the benchmark, we collect, annotate and release a new dataset that features videos with multiple people labeled with person tracks and articulated pose. A public centralized evaluation server is provided to allow the research community to evaluate on a held-out test set. Furthermore, we conduct an extensive experimental study on recent approaches to articulated pose tracking and provide analysis of the strengths and weaknesses of the state of the art. We envision that the proposed benchmark will stimulate productive research both by providing a large and representative training dataset as well as providing a platform to objectively evaluate and compare the proposed methods. The benchmark is freely accessible at https://posetrack.net/.
Exploit the Unknown Gradually: One-Shot Video-Based Person Re-Identification by Stepwise Learning
Authors: Yutian Lin, Xuanyi Dong, Yan Yan, Wanli Ouyang, Yi Yang Yu Wu
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018 (Main Conference Track), 2018

Abstract: We focus on the one-shot learning for video-based person re-Identification (re-ID). Unlabeled tracklets for the person re-ID tasks can be easily obtained by pre-processing, such as pedestrian detection and tracking. In this paper, we propose an approach to exploiting unlabeled tracklets by gradually but steadily improving the discriminative capability of the Convolutional Neural Network (CNN) feature representation via stepwise learning. We first initialize a CNN model using one labeled tracklet for each identity. Then we update the CNN model by the following two steps iteratively: 1. sample a few candidates with most reliable pseudo labels from unlabeled tracklets; 2. update the CNN model according to the selected data. Instead of the static sampling strategy applied in existing works, we propose a progressive sampling method to increase the number of the selected pseudo-labeled candidates step by step. We systematically investigate the way how we should select pseudo-labeled tracklets into the training set to make the best use of them. Notably, the rank-1 accuracy of our method outperforms the state-of-the-art method by 21.46 points (absolute, i.e., 62.67% vs. 41.21%) on the MARS dataset, and 16.53 points on the DukeMTMC-VideoReID dataset.
Pose-Robust Face Recognition via Deep Residual Equivariant Mapping
Authors: Yu Rong, Cheng Li, Xiaoou Tang, Chen Change Loy Kaidi Cao
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018 (Main Conference Track), 2018

Abstract: Face recognition achieves exceptional success thanks to the emergence of deep learning. However, many contemporary face recognition models still perform relatively poor in processing profile faces compared to frontal faces. A key reason is that the number of frontal and profile training faces are highly imbalanced - there are extensively more frontal training samples compared to profile ones. In addition, it is intrinsically hard to learn a deep representation that is geometrically invariant to large pose variations. In this study, we hypothesize that there is an inherent mapping between frontal and profile faces, and consequently, their discrepancy in the deep representation space can be bridged by an equivariant mapping. To exploit this mapping, we formulate a novel Deep Residual EquivAriant Mapping (DREAM) block, which is capable of adaptively adding residuals to the input deep representation to transform a profile face representation to a canonical pose that simplifies recognition. The DREAM block consistently enhances the performance of profile face recognition for many strong deep networks, including ResNet models, without deliberately augmenting training data of profile faces. The block is easy to use, light-weight, and can be implemented with a negligible computational overhead.
DecideNet: Counting Varying Density Crowds Through Attention Guided Detection and Density Estimation
Authors: Chenqiang Gao, Deyu Meng, Alexander G. Hauptmann Jiang Liu
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018 (Main Conference Track), 2018

Abstract: In real-world crowd counting applications, the crowd densities vary greatly in spatial and temporal domains. A detection based counting method will estimate crowds accurately in low density scenes, while its reliability in congested areas is downgraded. A regression based approach, on the other hand, captures the general density information in crowded regions. Without knowing the location of each person, it tends to overestimate the count in low density areas. Thus, exclusively using either one of them is not sufficient to handle all kinds of scenes with varying densities. To address this issue, a novel end-to-end crowd counting framework, named DecideNet (DEteCtIon and Density Estimation Network) is proposed. It can adaptively decide the appropriate counting mode for different locations on the image based on its real density conditions. DecideNet starts with estimating the crowd density by generating detection and regression based density maps separately. To capture inevitable variation in densities, it incorporates an attention module, meant to adaptively assess the reliability of the two types of estimations. The final crowd counts are obtained with the guidance of the attention module to adopt suitable estimations from the two kinds of density maps. Experimental results show that our method achieves state-of-the-art performance on three challenging crowd counting datasets.
LSTM Pose Machines
Authors: Jimmy Ren, Zhouxia Wang, Wenxiu Sun, Jinshan Pan, Jianbo Liu, Jiahao Pang, Liang Lin Yue Luo
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018 (Main Conference Track), 2018

Abstract: We observed that recent state-of-the-art results on single image human pose estimation were achieved by multi-stage Convolution Neural Networks (CNN). Notwithstanding the superior performance on static images, the application of these models on videos is not only computationally intensive, it also suffers from performance degeneration and flicking. Such suboptimal results are mainly attributed to the inability of imposing sequential geometric consistency, handling severe image quality degradation (e.g. motion blur and occlusion) as well as the inability of capturing the temporal correlation among video frames. In this paper, we proposed a novel recurrent network to tackle these problems. We showed that if we were to impose the weight sharing scheme to the multi-stage CNN, it could be re-written as a Recurrent Neural Network (RNN). This property decouples the relationship among multiple network stages and results in significantly faster speed in invoking the network for videos. It also enables the adoption of Long Short-Term Memory (LSTM) units between video frames. We found such memory augmented RNN is very effective in imposing geometric consistency among frames. It also well handles input quality degradation in videos while successfully stabilizes the sequential outputs. The experiments showed that our approach significantly outperformed current state-of-the-art methods on two large-scale video pose estimation benchmarks. We also explored the memory cells inside the LSTM and provided insights on why such mechanism would benefit the prediction for video-based pose estimations.
Disentangling Features in 3D Face Shapes for Joint Face Reconstruction and Recognition
Authors: Ronghang Zhu, Dan Zeng, Qijun Zhao, Xiaoming Liu Feng Liu
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018 (Main Conference Track), 2018

Abstract: This paper proposes an encoder-decoder network to disentangle shape features during 3D face shape reconstruction from single 2D images, such that the tasks of learning discriminative shape features for face recognition and reconstructing accurate 3D face shapes can be done simultaneously. Unlike existing 3D face reconstruction methods, our proposed method directly regresses dense 3D face shapes from single 2D images, and tackles identity and residual (i.e., non-identity) components in 3D face shapes explicitly and separately based on a composite 3D face shape model with latent representations. We devise a training process for the proposed network with a joint loss measuring both face identification error and 3D face shape reconstruction error. We develop a multi image 3D morphable model (3DMM) fitting method for multiple 2D images of a subject to construct training data. Comprehensive experiments have been done on MICC, BU3DFE, LFW and YTF databases. The results show that our method expands the capacity of 3DMM for capturing discriminative shape features and facial detail, and thus outperforms existing methods both in 3D face reconstruction accuracy and in face recognition accuracy.
Convolutional Sequence to Sequence Model for Human Dynamics
Authors: Zhen Zhang, Wee Sun Lee, Gim Hee Lee Chen Li
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018 (Main Conference Track), 2018

Abstract: Human motion modeling is a classic problem in com- puter vision and graphics. Challenges in modeling human motion include high dimensional prediction as well as extremely complicated dynamics.We present a novel approach to human motion modeling based on convolutional neural networks (CNN). The hierarchical structure of CNN makes it capable of capturing both spatial and temporal correlations effectively. In our proposed approach, a convolutional long-term encoder is used to encode the whole given motion sequence into a long-term hidden variable, which is used with a decoder to predict the remainder of the sequence. The decoder itself also has an encoder-decoder structure, in which the short-term encoder encodes a shorter sequence to a short-term hidden variable, and the spatial decoder maps the long and short-term hidden variable to motion predictions. By using such a model, we are able to capture both invariant and dynamic information of human motion, which results in more accurate predictions. Experiments show that our algorithm outperforms the state-of-the-art methods on the Human3.6M and CMU Motion Capture datasets. Our code is available at the project website
Gesture Recognition: Focus on the Hands
Authors: Ross Beveridge, Bruce A. Draper Pradyumna Narayana
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018 (Main Conference Track), 2018

Abstract: Gestures are a common form of human communication and important for human computer interfaces (HCI). Recent approaches to gesture recognition use deep learning methods, including multi-channel methods. We show that when spatial channels are focused on the hands, gesture recognition improves significantly, particularly when the channels are fused using a sparse network. Using this technique, we improve performance on the ChaLearn IsoGD dataset from a previous best of 67.71% to 82.07%, and on the NVIDIA dataset from 83.8% to 91.28%.
Crowd Counting via Adversarial Cross-Scale Consistency Pursuit
Authors: Yi Xu, Bingbing Ni, Minsi Wang, Jianguo Hu, Xiaokang Yang Zan Shen
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018 (Main Conference Track), 2018

Abstract: Crowd counting or density estimation is a challenging task in computer vision due to large scale variations, perspective distortions and serious occlusions, etc. Existing methods generally suffers from two issues: 1) the model averaging effects in multi-scale CNNs induced by the widely adopted L2 regression loss; and 2) inconsistent estimation across different scaled inputs. To explicitly address these issues, we propose a novel crowd counting (density estimation) framework called Adversarial Cross-Scale Consistency Pursuit (ACSCP). On one hand, a U-net structural network is designed to generate density map from input patch, and an adversarial loss is employed to shrink the solution onto a realistic subspace, thus attenuating the blurry effects of density map estimation. On the other hand, we design a novel scale-consistency regularizer which enforces that the sum up of the crowd counts from local patches (i.e., small scale) is coherent with the overall count of their region union (i.e., large scale). The above losses are integrated via a joint training scheme, so as to help boost density estimation performance by further exploring the collaboration between both objectives. Extensive experiments on four benchmarks have well demonstrated the effectiveness of the proposed innovations as well as the superior performance over prior art.
3D Human Pose Estimation in the Wild by Adversarial Learning
Authors: Wanli Ouyang, Xiaolong Wang, Jimmy Ren, Hongsheng Li, Xiaogang Wang Wei Yang
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018 (Main Conference Track), 2018

Abstract: Recently, remarkable advances have been achieved in 3D human pose estimation from monocular images because of the powerful Deep Convolutional Neural Networks (DCNNs). Despite their success on large-scale datasets collected in the constrained lab environment, it is difficult to obtain the 3D pose annotations for in-the-wild images. Therefore, 3D human pose estimation in the wild is still a challenge. In this paper, we propose an adversarial learning framework, which distills the 3D human pose structures learned from the fully annotated dataset to in-the-wild images with only 2D pose annotations. Instead of defining hard-coded rules to constrain the pose estimation results, we design a novel multi-source discriminator to distinguish the predicted 3D poses from the ground truth, which helps to enforce the pose estimator to generate anthropometrically valid poses even with images in the wild. We also observe that a carefully designed information source for the discriminator is essential to boost the performance. Thus, we design a geometric descriptor, which computes the pairwise relative locations and distances between body joints, as a new information source for the discriminator. The efficacy of our adversarial learning framework with the new geometric descriptor have been demonstrated through extensive experiments on two widely used public benchmarks. Our approach significantly improves the performance compared with previous state-of-the-art approaches.