GenAI4ELab/papercli-papers huggingface dataset:
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
Venue: ECCV 2020 (main)

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
Venue: ECCV 2020 (main)

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
Venue: ECCV 2020 (main)

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
Venue: ECCV 2020 (main)

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
Venue: ECCV 2020 (main)

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
Venue: ECCV 2020 (main)

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
Venue: ECCV 2020 (main)

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
Venue: ECCV 2020 (main)

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
Venue: ECCV 2020 (main)

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
Venue: ECCV 2020 (main)

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
Venue: ECCV 2020 (main)

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
Venue: ECCV 2020 (main)

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
Venue: ECCV 2020 (main)

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
Venue: ECCV 2020 (main)

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
Venue: ECCV 2020 (main)

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
Venue: ECCV 2020 (main)

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
Venue: ECCV 2020 (main)

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
Venue: ECCV 2020 (main)

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."