mtybilly/Instruct2DS huggingface dataset:
Corner Proposal Network for Anchor-free, Two-stage Object Detection
Authors: Kaiwen Duan, Lingxi Xie, Honggang Qi, Song Bai, Qingming Huang, Qi Tian
Published in: European Conference on Computer Vision (ECCV), 2020 (Main Conference Track), 2020

Abstract: Two-stage Object Detection","The goal of object detection is to determine the class and location of objects in an image. This paper proposes a novel anchor-free, two-stage framework which first extracts a number of object proposals by finding potential corner keypoint combinations and then assigns a class label to each proposal by a standalone classification stage. We demonstrate that these two stages are effective solutions for improving recall and precision, respectively, and they can be integrated into an end-to-end network. Our approach, dubbed Corner Proposal Network (CPN), enjoys the ability to detect objects of various scales and also avoids being confused by a large number of false-positive proposals. On the MS-COCO dataset, CPN achieves an AP of 49.2% which is competitive among state-of-the-art object detection methods. CPN also fits the scenario of computational efficiency, which achieves an AP of 41.6%/39.7% at 26.2/43.3 FPS, surpassing most competitors with the same inference speed. Code is available at https://github.com/Duankaiwen/CPNDet"
PhraseClick: Toward Achieving Flexible Interactive Segmentation by Phrase and Click
Authors: Henghui Ding, Scott Cohen, Brian Price, Xudong Jiang
Published in: European Conference on Computer Vision (ECCV), 2020 (Main Conference Track), 2020

Abstract: Existing interactive object segmentation methods mainly take spatial interactions such as bounding boxes or clicks as input. However, these interactions do not contain information about explicit attributes of the target-of-interest and thus cannot quickly specify what the selected object exactly is, especially when there are diverse scales of candidate objects or the target-of-interest contains multiple objects. Therefore, excessive user interactions are often required to reach desirable results. On the other hand, in existing approaches attribute information of objects is often not well utilized in interactive segmentation. We propose to employ phrase expressions as another interaction input to infer the attributes of target object. In this way, we can 1) leverage spatial clicks to locate the target object and 2) utilize semantic phrases to qualify the attributes of the target object. Specifically, the phrase expressions focus on ``what"" the target object is and the spatial clicks are in charge of ``where"" the target object is, which together help to accurately segment the target-of-interest with smaller number of interactions. Moreover, the proposed approach is flexible in terms of interaction modes and can efficiently handle complex scenarios by leveraging the strengths of each type of input. Our multi-modal phrase+click approach achieves new state-of-the-art performance on interactive segmentation. To the best of our knowledge, this is the first work to leverage both clicks and phrases for interactive segmentation."
Unified Multisensory Perception: Weakly-Supervised Audio-Visual Video Parsing
Authors: Yapeng Tian, Dingzeyu Li, Chenliang Xu
Published in: European Conference on Computer Vision (ECCV), 2020 (Main Conference Track), 2020

Abstract: In this paper, we introduce a new problem, named audio-visual video parsing, which aims to parse a video into temporal event segments and label them as either audible, visible, or both. Such a problem is essential for a complete understanding of the scene depicted inside a video. To facilitate exploration, we collect a Look, Listen, and Parse (LLP) dataset to investigate audio-visual video parsing in a weakly-supervised manner. This task can be naturally formulated as a Multimodal Multiple Instance Learning (MMIL) problem. Concretely, we propose a novel hybrid attention network to explore unimodal and cross-modal temporal contexts simultaneously. We develop an attentive MMIL pooling method to adaptively explore useful audio and visual content from different temporal extent and modalities. Furthermore, we discover and mitigate modality bias and noisy label issues with an individual-guided learning mechanism and label smoothing technique, respectively. Experimental results show that the challenging audio-visual video parsing can be achieved even with only video-level weak labels. Our proposed framework can effectively leverage unimodal and cross-modal temporal contexts and alleviate modality bias and noisy labels problems. "
Learning Delicate Local Representations for Multi-Person Pose Estimation
Authors: Yuanhao Cai, Zhicheng Wang, Zhengxiong Luo, Binyi Yin, Angang Du, Haoqian Wang, Xiangyu Zhang, Xinyu Zhou, Erjin Zhou, Jian Sun
Published in: European Conference on Computer Vision (ECCV), 2020 (Main Conference Track), 2020

Abstract: In this paper, we propose a novel method called Residual Steps Network (RSN). RSN aggregates features with the same spatial size (Intra-level features) efficiently to obtain delicate local representations, which retain rich low-level spatial information and result in precise keypoint localization. Additionally, we observe the output features contribute differently to final performance. To tackle this problem, we propose an efficient attention mechanism - Pose Refine Machine (PRM) to make a trade-off between local and global representations in output features and further refine the keypoint locations. Our approach won the 1st place of COCO Keypoint Challenge 2019 and achieves state-of-the-art results on both COCO and MPII benchmarks, without using extra training data and pretrained model. Our single model achieves 78.6 on COCO test-dev, 93.0 on MPII test dataset. Ensembled models achieve 79.2 on COCO test-dev, 77.1 on COCO test-challenge dataset. The source code is publicly available for further research at https://github.com/caiyuanhao1998/RSN/"
Learning to Plan with Uncertain Topological Maps
Authors: Edward Beeching, Jilles Dibangoye, Olivier Simonin, Christian Wolf
Published in: European Conference on Computer Vision (ECCV), 2020 (Main Conference Track), 2020

Abstract: We train an agent to navigate in 3D environments using a hierarchical strategy including a high-level graph based planner and a local policy. Our main contribution is a data driven learning based approach for planning under uncertainty in topological maps, requiring an estimate of shortest paths in valued graphs with a probabilistic structure. Whereas classical symbolic algorithms achieve optimal results on noise-less topologies, or optimal results in a probabilistic sense on graphs with probabilistic structure, we aim to show that machine learning can overcome missing information in the graph by taking into account rich high-dimensional node features, for instance visual information available at each location of the map. Compared to purely learned neural white box algorithms, we structure our neural model with an inductive bias for dynamic programming based shortest path algorithms, and we show that a particular parameterization of our neural model corresponds to the Bellman-Ford algorithm. By performing an empirical analysis of our method in simulated photo-realistic 3D environments, we demonstrate that the inclusion of visual features in the learned neural planner outperforms classical symbolic solutions for graph based planning. "
Neural Design Network: Graphic Layout Generation with Constraints
Authors: Hsin-Ying Lee, Lu Jiang, Irfan Essa, Phuong B Le, Haifeng Gong, Ming-Hsuan Yang, Weilong Yang
Published in: European Conference on Computer Vision (ECCV), 2020 (Main Conference Track), 2020

Abstract: Graphic design is essential for visual communication with layouts being fundamental to composing attractive designs. Layout generation differs from pixel-level image synthesis and is unique in terms of the requirement of mutual relations among the desired components. We propose a method for design layout generation that can satisfy user-specified constraints. The proposed neural design network (NDN) consists of three modules. The first module predicts a graph with complete relations from a graph with user-specified relations. The second module generates a layout from the predicted graph. Finally, the third module fine-tunes the predicted layout. Quantitative and qualitative experiments demonstrate that the generated layouts are visually similar to real design layouts. We also construct real designs based on predicted layouts for a better understanding of the visual quality. Finally, we demonstrate a practical application on layout recommendation."
Learning Open Set Network with Discriminative Reciprocal Points
Authors: Guangyao Chen, Limeng Qiao, Yemin Shi, Peixi Peng, Jia Li, Tiejun Huang, Shiliang Pu, Yonghong Tian
Published in: European Conference on Computer Vision (ECCV), 2020 (Main Conference Track), 2020

Abstract: Open set recognition is an emerging research area that aims to simultaneously classify samples from predefined classes and identify the rest as 'unknown'. In this process, one of the key challenges is to reduce the risk of generalizing the inherent characteristics of numerous unknown samples learned from a small amount of known data. In this paper, we propose a new concept, Reciprocal Point, which is the potential representation of the extra-class space corresponding to each known category. The sample can be classified to known or unknown by the otherness with reciprocal points. To tackle the open set problem, we offer a novel open space risk regularization term. Based on the bounded space constructed by reciprocal points, the risk of unknown is reduced through multi-category interaction. The novel learning framework called Reciprocal Point Learning (RPL), which can indirectly introduce the unknown information into the learner with only known classes, so as to learn more compact and discriminative representations. Moreover, we further construct a new large-scale challenging aircraft dataset for open set recognition: Aircraft 300 (Air-300). Extensive experiments on multiple benchmark datasets indicate that our framework is significantly superior to other existing approaches and achieves state-of-the-art performance on standard open set benchmarks."
Convolutional Occupancy Networks
Authors: Songyou Peng, Michael Niemeyer, Lars Mescheder, Marc Pollefeys, Andreas Geiger
Published in: European Conference on Computer Vision (ECCV), 2020 (Main Conference Track), 2020

Abstract: Recently, implicit neural representations have gained popularity for learning-based 3D reconstruction. While demonstrating promising results, most implicit approaches are limited to comparably simple geometry of single objects and do not scale to more complicated or large-scale scenes. The key limiting factor of implicit methods is their simple fully-connected network architecture which does not allow for integrating local information in the observations or incorporating inductive biases such as translational equivariance. In this paper, we propose Convolutional Occupancy Networks, a more flexible implicit representation for detailed reconstruction of objects and 3D scenes. By combining convolutional encoders with implicit occupancy decoders, our model incorporates inductive biases, enabling structured reasoning in 3D space. We investigate the effectiveness of the proposed representation by reconstructing complex geometry from noisy point clouds and low-resolution voxel representations. We empirically find that our method enables the fine-grained implicit 3D reconstruction of single objects, scales to large indoor scenes, and generalizes well from synthetic to real data."
Multi-person 3D Pose Estimation in Crowded Scenes Based on Multi-View Geometry
Authors: He Chen, Pengfei Guo, Pengfei Li, Gim Hee Lee, Gregory Chirikjian
Published in: European Conference on Computer Vision (ECCV), 2020 (Main Conference Track), 2020

Abstract: Epipolar constraints are at the core of feature matching and depth estimation in current multi-person multi-camera 3D human pose estimation methods. Despite the satisfactory performance of this formulation in sparser crowd scenes, its effectiveness is frequently challenged under denser crowd circumstances mainly due to two sources of ambiguity. The first is the mismatch of human joints resulting from the simple cues provided by the Euclidean distances between joints and epipolar lines. The second is the lack of robustness from the naive formulation of the problem as a least squares minimization. In this paper, we depart from the multi-person 3D pose estimation formulation, and instead reformulate it as crowd pose estimation. Our method consists of two key components: a graph model for fast cross-view matching, and a maximum a posteriori (MAP) estimator for the reconstruction of the 3D human poses. We demonstrate the effectiveness and superiority of our proposed method on four benchmark datasets."
TIDE: A General Toolbox for Identifying Object Detection Errors
Authors: Daniel Bolya, Sean Foley, James Hays, Judy Hoffman
Published in: European Conference on Computer Vision (ECCV), 2020 (Main Conference Track), 2020

Abstract: We introduce TIDE, a framework and associated toolbox for analyzing the sources of error in object detection and instance segmentation algorithms. Importantly, our framework is applicable across datasets and can be applied directly to output prediction files without required knowledge of the underlying prediction system. Thus, our framework can be used as a drop-in replacement for the standard mAP computation while providing a comprehensive analysis of each model's strengths and weaknesses. We segment errors into six types and, crucially, are the first to introduce a technique for measuring the contribution of each error in a way that isolates its effect on overall performance. We show that such a representation is critical for drawing accurate, comprehensive conclusions through in-depth analysis across 4 datasets and 7 recognition models."
PointContrast: Unsupervised Pre-training for 3D Point Cloud Understanding
Authors: Saining Xie, Jiatao Gu, Demi Guo, Charles R. Qi, Leonidas Guibas, Or Litany
Published in: European Conference on Computer Vision (ECCV), 2020 (Main Conference Track), 2020

Abstract: Arguably one of the top success stories of deep learning is transfer learning. The finding that pre-training a network on a rich source set (g, ImageNet) can help boost performance once fine-tuned on a usually much smaller target set, has been instrumental to many applications in language and vision. Yet, very little is known about its usefulness in 3D point cloud understanding. We see this as an opportunity considering the effort required for annotating data in 3D. In this work, we aim at facilitating research on 3D representation learning. Different from previous works, we focus on high-level scene understanding tasks. To this end, we select a suit of diverse datasets and tasks to measure the effect of unsupervised pre-training on a large source set of 3D scenes. Our findings are extremely encouraging: using a unified triplet of architecture, source dataset, and contrastive loss for pre-training, we achieve improvement over recent best results in segmentation and detection across 6 different benchmarks for indoor and outdoor, real and synthetic datasets -- demonstrating that the learned representation can generalize across domains. Furthermore, the improvement was similar to supervised pre-training, suggesting that future efforts should favor scaling data collection over more detailed annotation. We hope these findings will encourage more research on unsupervised pretext task design for 3D deep learning."
DSA: More Efficient Budgeted Pruning via Differentiable Sparsity Allocation
Authors: Xuefei Ning, Tianchen Zhao, Wenshuo Li, Peng Lei, Yu Wang, Huazhong Yang
Published in: European Conference on Computer Vision (ECCV), 2020 (Main Conference Track), 2020

Abstract: Budgeted pruning is the problem of pruning under resource constraints. In budgeted pruning, how to distribute the resources across layers (i.e., sparsity allocation) is the key problem. Traditional methods solve it by discretely searching for the layer-wise pruning ratios, which lacks efficiency. In this paper, we propose Differentiable Sparsity Allocation (DSA), an efficient end-to-end budgeted pruning flow. Utilizing a novel differentiable pruning process, DSA finds the layer-wise pruning ratios with gradient-based optimization. It allocates sparsity in continuous space, which is more efficient than methods based on discrete evaluation and search. Furthermore, DSA could work in a pruning-from-scratch manner, whereas traditional budgeted pruning methods are applied to pre-trained models. Experimental results on CIFAR-10 and ImageNet show that DSA could achieve superior performance than current iterative budgeted pruning methods, and shorten the time cost of the overall pruning process by at least 1.5x in the meantime."
Circumventing Outliers of AutoAugment with Knowledge Distillation
Authors: Longhui Wei, An Xiao, Lingxi Xie, Xiaopeng Zhang, Xin Chen, Qi Tian
Published in: European Conference on Computer Vision (ECCV), 2020 (Main Conference Track), 2020

Abstract: AutoAugment has been a powerful algorithm that improves the accuracy of many vision tasks, yet it is sensitive to the operator space as well as hyper-parameters, and an improper setting may degenerate network optimization. This paper delves deep into the working mechanism, and reveals that AutoAugment may remove part of discriminative information from the training image and so insisting on the ground-truth label is no longer the best option. To relieve the inaccuracy of supervision, we make use of knowledge distillation that refers to the output of a teacher model to guide network training. Experiments are performed in standard image classification benchmarks, and demonstrate the effectiveness of our approach in suppressing noise of data augmentation and stabilizing training. Upon the cooperation of knowledge distillation and AutoAugment, we claim the new state-of-the-art on ImageNet classification with a top-1 accuracy of 85.7%."
S2DNet: Learning Image Features for Accurate Sparse-to-Dense Matching
Authors: Hugo Germain, Guillaume Bourmaud, Vincent Lepetit
Published in: European Conference on Computer Vision (ECCV), 2020 (Main Conference Track), 2020

Abstract: Establishing robust and accurate correspondences is a fundamental backbone to many computer vision algorithms. While recent learning-based feature matching methods have shown promising results in providing robust correspondences under challenging conditions, they are often limited in terms of precision. In this paper, we introduce S2DNet, a novel feature matching pipeline, designed and trained to efficiently establish both robust and accurate correspondences. By leveraging a sparse-to-dense matching paradigm, we cast the correspondence learning problem as a supervised classification task to learn to output highly peaked correspondence maps. We show that S2DNet achieves state-of-the-art results on the HPatches benchmark, as well as on several long-term visual localization datasets. "
RTM3D: Real-time Monocular 3D Detection from Object Keypoints for Autonomous Driving
Authors: Peixuan Li, Huaici Zhao, Pengfei Liu, Feidao Cao
Published in: European Conference on Computer Vision (ECCV), 2020 (Main Conference Track), 2020

Abstract: In this work, we propose an efficient and accurate monocular 3D detection framework in single shot. Most successful 3D detectors take the projection constraint from the 3D bounding box to the 2D box as an important component. Four edges of a 2D box provide only four constraints and the performance deteriorates dramatically with the small error of the 2D detector. Different from these approaches, our method predicts the nine perspective keypoints of a 3D bounding box in image space, and then utilize the geometric relationship of 3D and 2D perspectives to recover the dimension, location, and orientation in 3D space. In this method, the properties of the object can be predicted stably even when the estimation of keypoints is very noisy, which enables us to obtain fast detection speed with a small architecture. Training our method only uses the 3D properties of the object without any extra annotations, category-specific 3D shape priors, or depth maps. Our method is the first real-time system (FPS>24) for monocular image 3D detection while achieves state-of-the-art performance on the KITTI benchmark."
Video Object Segmentation with Episodic Graph Memory Networks
Authors: Xiankai Lu, Wenguan Wang, Martin Danelljan, Tianfei Zhou, Jianbing Shen, Luc Van Gool
Published in: European Conference on Computer Vision (ECCV), 2020 (Main Conference Track), 2020

Abstract: How to make a segmentation model efficiently adapt to a specific video as well as online target appearance variations is a fun- damental issue in the field of video object segmentation. In this work, a graph memory network is developed to address the novel idea of “learning to update the segmentation model”. Specifically, we exploit an episodic memory network, organized as a fully connected graph, to store frames as nodes and capture cross-frame correlations by edges. Further, learnable controllers are embedded to ease memory reading and writing, as well as maintain a fixed memory scale. The structured, external memory design enables our model to comprehensively mine and quickly store new knowl- edge, even with limited visual information, and the differentiable memory controllers slowly learn an abstract method for storing useful represen- tations in the memory and how to later use these representations for prediction, via gradient descent. In addition, the proposed graph mem- ory network yields a neat yet principled framework, which can generalize well to both one-shot and zero-shot video object segmentation tasks. Ex- tensive experiments on four challenging benchmark datasets verify that our graph memory network is able to facilitate the adaptation of the segmentation network for case-by-case video object segmentation."
Rethinking Bottleneck Structure for Efficient Mobile Network Design
Authors: Daquan Zhou, Qibin Hou, Yunpeng Chen, Jiashi Feng, Shuicheng Yan
Published in: European Conference on Computer Vision (ECCV), 2020 (Main Conference Track), 2020

Abstract: The inverted residual block is dominating architecture design for mobile networks recently. It changes the classic residual bottleneck by introducing two design rules: learning inverted residuals and using linear bottlenecks. In this paper, we rethink the necessity of such design change and find it may bring risks of information loss and gradient confusion. We thus propose to flip the structure and present a novel bottleneck design, called sandglass block, that performs identity mapping and spatial transformation at higher dimensions and thus alleviates information loss and gradient confusion effectively. Extensively experiments demonstrate that, different from the common belief, such bottleneck structure is indeed more beneficial than the inverted ones for mobile networks. In ImageNet classification, by simply replacing the inverted residual block with sandglass block, without increasing parameters and computations, the classification accuracy can be improved by more than 1.7% over MobileNetV2. On Pascal VOC 2007 test set, we observe that there is also 0.9% mAP improvement in object detection. We further verify the effectiveness of sandglass block by adding it into the search space of neural architecture search method DARTS. With 25% parameter reduction, the classification accuracy is improved by 0.13% over previous DARTS models. Code will be made publicly available."
Side-Tuning: A Baseline for Network Adaptation via Additive Side Networks
Authors: Jeffrey O. Zhang, Alexander Sax, Amir Zamir, Leonidas Guibas, Jitendra Malik
Published in: European Conference on Computer Vision (ECCV), 2020 (Main Conference Track), 2020

Abstract: When training a neural network for a desired task, one may prefer to adapt a pre-trained network rather than starting from randomly initialized weights. Adaptation can be useful in cases when training data is scarce, when a single learner needs to perform multiple tasks, or when one wishes to encode priors in the network. The most commonly employed approaches for network adaptation are fine-tuning and using the pre-trained network as a fixed feature extractor, among others.  In this paper, we propose a straightforward alternative: side-tuning. Side-tuning adapts a pre-trained network by training a lightweight ""side"" network that is fused with the (unchanged) pre-trained network via summation. This simple method works as well as or better than existing solutions and it resolves some of the basic issues with fine-tuning, fixed features, and other common approaches. In particular, side-tuning is less prone to overfitting, is asymptotically consistent, and does not suffer from catastrophic forgetting in incremental learning. We demonstrate the performance of side-tuning under a diverse set of scenarios, including incremental learning (iCIFAR, iTaskonomy), reinforcement learning, imitation learning (visual navigation in Habitat), NLP question-answering (SQuAD v2), and single-task transfer learning (Taskonomy), with consistently promising results."
Towards Part-aware Monocular 3D Human Pose Estimation: An Architecture Search Approach
Authors: Zerui Chen, Yan Huang, Hongyuan Yu, Bin Xue, Ke Han, Yiru Guo, Liang Wang
Published in: European Conference on Computer Vision (ECCV), 2020 (Main Conference Track), 2020

Abstract: Even though most existing monocular 3D pose estimation approaches achieve very competitive results, they ignore the heterogeneity among human body parts by estimating them with the same network architecture. To accurately estimate 3D poses of different body parts, we attempt to build a part-aware 3D pose estimator by searching a set of network architectures. Consequently, our model automatically learns to select a suitable architecture to estimate each body part. Compared to models built on the commonly used ResNet-50 backbone, it reduces 62\% parameters and achieves better performance. With roughly the same computational complexity as previous models, our approach achieves state-of-the-art results on both the single-person and multi-person 3D pose estimation benchmarks."
REVISE: A Tool for Measuring and Mitigating Bias in Visual Datasets
Authors: Angelina Wang, Arvind Narayanan, Olga Russakovsky
Published in: European Conference on Computer Vision (ECCV), 2020 (Main Conference Track), 2020

Abstract: Machine learning models are known to perpetuate and even amplify the biases present in the data. However, these data biases frequently do not become apparent until after the models are deployed. To tackle this issue and to enable the preemptive analysis of large-scale dataset, we present our tool. REVISE (REvealing VIsual biaSEs) is a tool that assists in the investigation of a visual dataset, surfacing potential biases currently along three dimensions: (1) object-based, (2) gender-based, and (3) geography-based. Object-based biases relate to size, context, or diversity of object representation. Gender-based metrics aim to reveal the stereotypical portrayal of people of different genders. Geography-based analyses consider the representation of different geographic locations. REVISE sheds light on the dataset along these dimensions; the responsibility then lies with the user to consider the cultural and historical context, and to determine which of the revealed biases may be problematic. The tool then further assists the user by suggesting actionable steps that may be taken to mitigate the revealed biases. Overall, the key aim of our work is to tackle the machine learning bias problem early in the pipeline. REVISE is available at https://github.com/princetonvisualai/revise-tool."
Contrastive Learning for Weakly Supervised Phrase Grounding
Authors: Tanmay Gupta, Arash Vahdat, Gal Chechik, Xiaodong Yang, Jan Kautz, Derek Hoiem
Published in: European Conference on Computer Vision (ECCV), 2020 (Main Conference Track), 2020

Abstract: Phrase grounding, the problem of associating image regions to caption words, is a crucial component of vision-language tasks. We show that phrase grounding can be learned by optimizing word-region attention to maximize a lower bound on mutual information between images and caption words. Given pairs of images and captions, we maximize compatibility of the attention-weighted regions and the words in the corresponding caption, compared to non-corresponding pairs of images and captions. A key idea is to construct effective negative captions for learning through language model guided word substitutions. Training with our negatives yields a $\sim10\%$ absolute gain in accuracy over randomly-sampled negatives from the training data. Our weakly supervised phrase grounding model trained on COCO-Captions shows a healthy gain of $5.7\%$ to achieve $76.7\%$ accuracy on Flickr30K Entities benchmark. Our code and project material will be available at http://tanmaygupta.info/info-ground."
Collaborative Learning of Gesture Recognition and 3D Hand Pose Estimation with Multi-Order Feature Analysis
Authors: Siyuan Yang, Jun Liu, Shijian Lu, Meng Hwa Er, Alex C. Kot
Published in: European Conference on Computer Vision (ECCV), 2020 (Main Conference Track), 2020

Abstract: Gesture recognition and 3D hand pose estimation are two highly correlated tasks, yet they are often handled separately. In this paper, we present a novel collaborative learning network for joint gesture recognition and 3D hand pose estimation. The proposed network exploits joint-aware features that are crucial for both tasks, with which gesture recognition and 3D hand pose estimation boost each other to learn highly discriminative features and models. In addition, a novel multi-order feature analysis method is introduced which learns posture and multi-order motion information from the intermediate feature maps of videos effectively and efficiently. Due to the exploitation of joint-aware features in common, the proposed technique is capable of learning gesture recognition and 3D hand pose estimation even when only gesture or pose labels are available, and this enables weakly supervised network learning with much reduced data labeling efforts. Extensive experiments show that our proposed method achieves superior gesture recognition and 3D hand pose estimation performance as compared with the state-of-the-art. Codes and models will be released upon the paper acceptance. "
Making an Invisibility Cloak: Real World Adversarial Attacks on Object Detectors
Authors: Zuxuan Wu, Ser-Nam Lim, Larry S. Davis, Tom Goldstein
Published in: European Conference on Computer Vision (ECCV), 2020 (Main Conference Track), 2020

Abstract: We present a systematic study of adversarial attacks on state-of-the-art object detection frameworks. Using standard detection datasets, we train patterns that suppress the objectness scores produced by a range of commonly used detectors, and ensembles of detectors. Through extensive experiments, we benchmark the effectiveness of adversarially trained patches under both white-box and black-box settings, and quantify transferability of attacks between datasets, object classes, and detector models. Finally, we present a detailed study of physical world attacks using printed posters and wearable clothes, and rigorously quantify the performance of such attacks with different metrics."
TuiGAN: Learning Versatile Image-to-Image Translation with Two Unpaired Images
Authors: Jianxin Lin, Yingxue Pang, Yingce Xia, Zhibo Chen, Jiebo Luo
Published in: European Conference on Computer Vision (ECCV), 2020 (Main Conference Track), 2020

Abstract: An unsupervised image-to-image translation (UI2I) task deals with learning a mapping between two domains without paired images. While existing UI2I methods usually require numerous unpaired images from different domains for training, there are many scenarios where training data is quite limited. In this paper, we argue that even if each domain contains a single image, UI2I can still be achieved. To this end, we propose TuiGAN, a generative model that is trained on only two unpaired images and amounts to one-shot unsupervised learning. With TuiGAN, an image is translated in a coarse-to-fine manner where the generated image is gradually refined from global structures to local details. We conduct extensive experiments to verify that our versatile method can outperform strong baselines on a wide variety of UI2I tasks. Moreover, TuiGAN is capable of achieving comparable performance with the state-of-the-art UI2I models trained with sufficient data."
Semi-Siamese Training for Shallow Face Learning
Authors: Hang Du, Hailin Shi, Yuchi Liu, Jun Wang, Zhen Lei, Dan Zeng, Tao Mei
Published in: European Conference on Computer Vision (ECCV), 2020 (Main Conference Track), 2020

Abstract: Most existing public face datasets, such as MS-Celeb-1M and VGGFace2, provide abundant information in both breadth (large number of IDs) and depth (sufficient number of samples) for training. However, in many real-world scenarios of face recognition, the training dataset is limited in depth, $ extit{i.e.}$ only two face images are available for each ID. $ extit{We define this situation as Shallow Face Learning, and find it problematic with existing training methods.}$ Unlike deep face data, the shallow face data lacks intra-class diversity. As such, it can lead to collapse of feature dimension and consequently the learned network can easily suffer from degeneration and over-fitting in the collapsed dimension. In this paper, we aim to address the problem by introducing a novel training method named Semi-Siamese Training (SST). A pair of Semi-Siamese networks constitute the forward propagation structure, and the training loss is computed with an updating gallery queue, conducting effective optimization on shallow training data. Our method is developed without extra-dependency, thus can be flexibly integrated with the existing loss functions and network architectures. Extensive experiments on various benchmarks of face recognition show the proposed method significantly improves the training, not only in shallow face learning, but also for conventional deep face data."
GAN Slimming: All-in-One GAN Compression by A Unified Optimization Framework
Authors: Haotao Wang, Shupeng Gui, Haichuan Yang, Ji Liu, Zhangyang Wang
Published in: European Conference on Computer Vision (ECCV), 2020 (Main Conference Track), 2020

Abstract: Generative adversarial networks (GANs) have gained increasing popularity in various computer vision applications, and recently start to be deployed to resource-constrained mobile devices. Similar to other deep models, state-of-the-art GANs also suffer from high parameter complexities. That has recently motivated the exploration of compressing GANs (usually generators). Compared to the vast literature and prevailing success in compressing deep classifiers, the study of GAN compression remains in its infancy, so far leveraging individual compression techniques instead of more sophisticated combinations. We observe that due to the notorious instability of training GANs, heuristically stacking different compression techniques will result in unsatisfactory results. To this end, we propose the first end-to-end optimization framework combining multiple compression means for GAN compression, dubbed GAN Slimming (GS). GS seamlessly integrates three mainstream compression techniques: model distillation, channel pruning and quantization, together with the GAN minimax objective, into one unified optimization form, that can be efficiently optimized from end to end. Without bells and whistles, GS largely outperforms existing options in compressing image-to-image translation GANs. Specifically, we apply GS to compress CartoonGAN, a state-of-the-art style transfer network, by up to $47 imes$ times, with minimal visual quality degradation. Codes and pre-trained models can be found at https://github.com/TAMU-VITA/GAN-Slimming."
Human Interaction Learning on 3D Skeleton Point Clouds for Video Violence Recognition
Authors: Yukun Su, Guosheng Lin, Jinhui Zhu, Qingyao Wu
Published in: European Conference on Computer Vision (ECCV), 2020 (Main Conference Track), 2020

Abstract: This paper introduces a new method for recognizing violent behavior by learning contextual relationships between related people from human skeleton points. Unlike previous work, we first formulate 3D skeleton point clouds from human skeleton sequences extracted from videos and then perform interaction learning on these 3D skeleton point clouds. A novel extbf{S}keleton extbf{P}oints extbf{I}nteraction extbf{L}earning (SPIL) module, is proposed to model the interactions between skeleton points. Specifically, by constructing a specific weight distribution strategy between local regional points, SPIL aims to selectively focus on the most relevant parts of them based on their features and spatial-temporal position information. In order to capture diverse types of relation information, a multi-head mechanism is designed to aggregate different features from independent heads to jointly handle different types of relationships between points. Experimental results show that our model outperforms the existing networks and achieves new state-of-the-art performance on video violence datasets."
Binarized Neural Network for Single Image Super Resolution
Authors: Jingwei Xin, Nannan Wang, Xinrui Jiang, Jie Li, Heng Huang, Xinbo Gao
Published in: European Conference on Computer Vision (ECCV), 2020 (Main Conference Track), 2020

Abstract: Lighter model and faster inference are the focus of current single image super-resolution (SISR) research. However, existing methods are still hard to be applied in real-world applications due to the requirement of its heavy computation. Model quantization is an effective way to significantly reduce model size and computation time. We propose a simple but effective binary neural networks (BNN) based SISR model with a novel binarization scheme. Specially, we design a bit-accumulation mechanism to approximate the full-precision values, which could realize the approximation to the full precision number by accumulating the multi-layer's one-bit values.The proposed BNN-based SISR method could achieve superior performance with lower computational complexity and less model parameters. Extensive experiments show that the proposed model outperforms the state-of-the-art methods (binarization methods such as BNN, DoReFa-Net and ABC-Net) by large margins on 4 benchmark datasets, specially by average more than 0.8 dB in terms of Peak Signal-to-Noise Ratio (PSNR) on Set5 dataset."
Axial-DeepLab: Stand-Alone Axial-Attention for Panoptic Segmentation
Authors: Huiyu Wang, Yukun Zhu, Bradley Green, Hartwig Adam, Alan Yuille, Liang-Chieh Chen
Published in: European Conference on Computer Vision (ECCV), 2020 (Main Conference Track), 2020

Abstract: Convolution exploits locality for efficiency at a cost of missing long range context. Self-attention has been adopted to augment CNNs with non-local interactions. Recent works prove it possible to stack self-attention layers to obtain a fully attentional network by restricting the attention to a local region. In this paper, we attempt to remove this constraint by factorizing 2D self-attention into two 1D self-attentions. This reduces computation complexity and allows performing attention within a larger or even global region. In companion, we also propose a position-sensitive self-attention design. Combining both yields our position-sensitive axial-attention layer, a novel building block that one could stack to form axial-attention models for image classification and dense prediction. We demonstrate the effectiveness of our model on four large-scale datasets. In particular, our model outperforms all existing stand-alone self-attention models on ImageNet. Our Axial-DeepLab improves 2.8% PQ over bottom-up state-of-the-art on COCO test-dev. This previous state-of-the-art is attained by our small variant that is 3.8x parameter-efficient and 27x computation-efficient. Axial-DeepLab also achieves state-of-the-art results on Mapillary Vistas and Cityscapes."
Adaptive Computationally Efficient Network for Monocular 3D Hand Pose Estimation
Authors: Zhipeng Fan, Jun Liu, Yao Wang
Published in: European Conference on Computer Vision (ECCV), 2020 (Main Conference Track), 2020

Abstract: 3D hand pose estimation is an important task for a wide range of real-world applications. Existing works in this domain mainly focus on designing advanced algorithms to achieve high pose estimation accuracy. However, besides accuracy, the computation efficiency that affects the computation speed and power consumption is also crucial for real-world applications. In this paper, we investigate the problem of reducing the overall computation cost yet maintaining the high accuracy for 3D hand pose estimation from video sequences. A novel model, called Adaptive Computationally Efficient (ACE) network, is proposed, which takes advantage of a Gaussian kernel based Gate Module to dynamically switch the computation between a light model and a heavy network for feature extraction. Our model employs the light model to compute efficient features for most of the frames and invokes the heavy model only when necessary. Combined with the temporal context, the proposed model accurately estimates the 3D hand pose. We evaluate our model on two publicly available datasets, and achieve state-of-the-art performance at only 22% of the computation cost compared to the ordinary temporal models."
Chained-Tracker: Chaining Paired Attentive Regression Results for End-to-End Joint Multiple-Object Detection and Tracking
Authors: Jinlong Peng, Changan Wang, Fangbin Wan, Yang Wu, Yabiao Wang, Ying Tai, Chengjie Wang, Jilin Li, Feiyue Huang, Yanwei Fu
Published in: European Conference on Computer Vision (ECCV), 2020 (Main Conference Track), 2020

Abstract: Existing Multiple-Object Tracking (MOT) methods either follow the tracking-by-detection paradigm to conduct object detection, feature extraction and data association separately, or have two of the three subtasks integrated to form a partially end-to-end solution. Going beyond these sub-optimal frameworks, we propose a simple online model named Chained-Tracker (CTracker), which naturally integrates all the three subtasks into an end-to-end solution (the first as far as we know). It chains paired bounding boxes regression results estimated from overlapping nodes, of which each node covers two adjacent frames. The paired regression is made attentive by object-attention (brought by a detection module) and identity-attention (ensured by an ID verification module). The two major novelties: chained structure and paired attentive regression, make CTracker simple, fast and effective, setting new MOTA records on MOT16 and MOT17 challenge datasets (67.6 and 66.6, respectively), without relying on any extra training data. The source code of CTracker can be found at: github.com/pjl1995/CTracker."
Distribution-Balanced Loss for Multi-Label Classification in Long-Tailed Datasets
Authors: Tong Wu, Qingqiu Huang, Ziwei Liu, Yu Wang, Dahua Lin
Published in: European Conference on Computer Vision (ECCV), 2020 (Main Conference Track), 2020

Abstract: We present a new loss function called Distribution-Balanced Loss for the multi-label recognition problems that exhibit long-tailed class distributions. Compared to conventional single-label classification problem, multi-label recognition problems are often more challenging due to two significant issues, namely the co-occurrence of labels and the dominance of negative labels (when treated as multiple binary classification problems). The Distribution-Balanced Loss tackles these issues through two key modifications to the standard binary cross-entropy loss: 1) a new way to re-balance the weights that takes into account the impact caused by label co-occurrence, and 2) a negative tolerant regularization to mitigate the over-suppression of negative labels. Experiments on both Pascal VOC and COCO show that the models trained with this new loss function achieve significant performance gains over existing methods. "
Hamiltonian Dynamics for Real-World Shape Interpolation
Authors: Marvin Eisenberger, Daniel Cremers
Published in: European Conference on Computer Vision (ECCV), 2020 (Main Conference Track), 2020

Abstract: We revisit the classical problem of 3D shape interpolation and propose a novel, physically plausible approach based on Hamiltonian dynamics. While most prior work focuses on synthetic input shapes, our formulation is designed to be applicable to real-world scans with imperfect input correspondences and various types of noise. To that end, we use recent progress on dynamic thin shell simulation and divergence-free shape deformation and combine them to address the inverse problem of finding a plausible intermediate sequence for two input shapes. In comparison to prior work that mainly focuses on small distortion of consecutive frames, we explicitly model volume preservation and momentum conservation, as well as an anisotropic local distortion model. We argue that, in order to get a robust interpolation for imperfect inputs, we need to model the input noise explicitly which results in an alignment based formulation. Finally, we show a qualitative and quantitative improvement over prior work on a broad range of synthetic and scanned data. Besides being more robust to noisy inputs, our method yields exactly volume preserving intermediate shapes, avoids self-intersections and is scalable to high resolution scans."
Learning to Scale Multilingual Representations for Vision-Language Tasks
Authors: Andrea Burns, Donghyun Kim, Derry Wijaya, Kate Saenko, Bryan A. Plummer
Published in: European Conference on Computer Vision (ECCV), 2020 (Main Conference Track), 2020

Abstract: Current multilingual vision-language models either require a large number of additional parameters for each supported language, or suffer performance degradation as languages are added. In this paper, we propose a Scalable Multilingual Aligned Language Representation (SMALR) that supports many languages with few model parameters without sacrificing downstream task performance. SMALR learns a fixed size language-agnostic representation for most words in a multilingual vocabulary, keeping language-specific features for just a few. We use a masked cross-language modeling loss to align features with context from other languages. Additionally, we propose a cross-lingual consistency module that ensures predictions made for a query and its machine translation are comparable. The effectiveness of SMALR is demonstrated with ten diverse languages, over twice the number supported in vision-language tasks to date. We evaluate on multilingual image-sentence retrieval and outperform prior work by 3-4% with less than 1/5th the training parameters compared to other word embedding methods."
Multi-modal Transformer for Video Retrieval
Authors: Valentin Gabeur, Chen Sun, Karteek Alahari, Cordelia Schmid
Published in: European Conference on Computer Vision (ECCV), 2020 (Main Conference Track), 2020

Abstract: The task of retrieving video content relevant to natural language queries plays a critical role in effectively handling internet-scale datasets. Most of the existing methods for this caption-to-video retrieval problem do not fully exploit cross-modal cues present in video. Furthermore, they aggregate per-frame visual features with limited or no temporal information. In this paper, we present a multi-modal transformer to jointly encode the different modalities in video, which allows each of them to attend to the others. The transformer architecture is also leveraged to encode and model the temporal information. On the natural language side, we investigate the best practices to jointly optimize the language embedding together with the multi-modal transformer. This novel framework allows us to establish state-of-the-art results for video retrieval on three datasets. More details are available at http://thoth.inrialpes.fr/research/MMT."