pwc-archive/papers-with-abstracts huggingface dataset:
Guided Saliency Feature Learning for Person Re-identification in Crowded Scenes
Authors: Lingxiao He, Wu Liu
Date: 2020-08-01 00:00:00
Proceeding: ECCV 2020 8
Tasks: Person Re-Identification

Person Re-identification (Re-ID) in crowed scenes is a challenging problem, where people are frequently partially occluded by objects and other people. However, few studies have provided flexible solutions to re-identifying people in an image containing a partial occlusion body part. In this paper, we propose a simple occlusion-aware approach to address the problem. The proposed method first leverages a fully convolutional network to generate spatial features. And then we design a combination of a pose-guided and mask-guided layer to generate saliency heatmap to further guide discriminative feature learning. More importantly, we propose a new matching approach, called Guided Adaptive Spatial Matching (GASM), which expects that each spatial feature in the query can find the most similar spatial features of a person in a gallery to match. Especially, We use the saliency heatmap to guide the adaptive spatial matching by assigning the foreground human parts with larger weights adaptively. The effectiveness of the proposed GASM is demonstrated on two occluded person datasets: Crowd REID (51.52\%) and Occluded REID (80.25\%) and three benchmark person datasets: Market1501 (95.31\%), DukeMTMC-reID (88.12\%) and MSMT17 (79.52\%). Additionally, GASM achieves good performance on cross-domain person Re-ID. The code and models are available at https://github.com/JDAI-CV/fast-reid/blob/master/projects/CrowdReID.
Asymmetric Two-Stream Architecture for Accurate RGB-D Saliency Detection
Authors: Miao Zhang, Sun Xiao Fei, Jie Liu, Shuang Xu, Yongri Piao, Huchuan Lu
Date: 2020-08-01 00:00:00
Proceeding: ECCV 2020 8
Tasks: Saliency Detection, Thermal Image Segmentation, Vocal Bursts Valence Prediction

Most existing RGB-D saliency detection methods adopt symmetric two-stream architectures for learning discriminative RGB and depth representations. In fact, there is another level of ambiguity that is often overlooked: if RGB and depth data are necessary to fit into the same network. In this paper, we propose an asymmetric two-stream architecture taking account of the inherent differences between RGB and depth data for saliency detection. First, we design a flow ladder module (FLM) for the RGB stream to fully extract global and local information while maintaining the saliency details. This is achieved by constructing four detail-transfer branches, each of which preserves the detail information and receives global location information from representations of other vertical parallel branches in an evolutionary way. Second, we propose a novel depth attention module (DAM) to ensure depth features with high discriminative power in location and spatial structure being effectively utilized when combined with RGB features in challenging scenes. The depth features can also discriminatively guide the RGB features via our proposed DAM to precisely locate the salient objects. Extensive experiments demonstrate that our method achieves superior performance over 13 state-of-the-art RGB-D approaches on the 7 datasets. Our code will be publicly available.
Explaining Image Classifiers using Statistical Fault Localization
Authors: Youcheng Sun, Hana Chockler, Xiaowei Huang, Daniel Kroening
Date: 2020-08-01 00:00:00
Proceeding: ECCV 2020 8
Tasks: Fault localization

The black-box nature of deep neural networks (DNNs) makes it impossible to understand why a particular output is produced, creating demand for “Explainable AI”. In this paper, we show that statistical fault localization (SFL) techniques from software engineering deliver high quality explanations of the outputs of DNNs, where we define an explanation as a minimal subset of features sufficient for making the same decision as for the original input. We present an algorithm and a tool called DeepCover, which synthesizes a ranking of the features of the inputs using SFL and constructs explanations for the decisions of the DNN based on this ranking. We compare explanations produced by DeepCover with those of the state-of-the-art tools GradCAM, LIME, SHAP, RISE and Extremal and show that explanations generated by DeepCover are consistently better across a broad set of experiments. On a benchmark set with known ground truth, DeepCover achieves 76.7% accuracy, which is 6% better than the second best Extremal.
Deep Graph Matching via Blackbox Differentiation of Combinatorial Solvers
Authors: Michal Rolínek, Paul Swoboda, Dominik Zietlow, Anselm Paulus, Vít Musil, Georg Martius
Date: 2020-08-01 00:00:00
Proceeding: ECCV 2020 8
Tasks: Combinatorial Optimization, Deep Learning, Graph Matching

Building on recent progress at the intersection of combinatorial optimization and deep learning, we propose an end-to-end trainable architecture for deep graph matching that contains unmodified combinatorial solvers. Using the presence of heavily optimized combinatorial solvers together with some improvements in architecture design, we advance state-of-the-art on deep graph matching benchmarks for keypoint correspondence. In addition, we highlight conceptual advantages of incorporating solvers into deep learning architectures, such as the possibility of post-processing with a strong multi-graph matching solver or the indifference to changes in the training setting. Finally, we propose two new challenging experimental setups.
Learning Video Representations by Transforming Time
Authors: Simon Jenni, Givi Meishvili, Paolo Favaro
Date: 2020-08-01 00:00:00
Proceeding: ECCV 2020 8
Tasks: Action Recognition, Self-Supervised Learning

We introduce a novel self-supervised learning approach to learn representations of videos that are responsive to changes in the motion dynamics. Our representations can be learned from data without human annotation and provide a substantial boost to the training of neural networks on small labeled data sets for tasks such as action recognition, which require to accurately distinguish the motion of objects. We promote an accurate learning of motion without human annotation by training a neural network to discriminate a video sequence from its temporally transformed versions. To learn to distinguish non-trivial motions, the design of the transformations is based on two principles: 1) To deﬁne clusters of motions based on time warps of diﬀerent magnitude; 2) To ensure that the discrimination is feasible only by observing and analyzing as many image frames as possible. Thus, we introduce the following transformations: forward-backward playback, random frame skipping, and uniform frame skipping. Our experiments show that networks trained with the proposed method yield representations with improved transfer performance for action recognition on UCF101 and HMDB51.
Variational Connectionist Temporal Classification
Authors: Linlin Chao, Jingdong Chen, Wei Chu
Date: 2020-08-01 00:00:00
Proceeding: ECCV 2020 8
Tasks: Classification, General Classification, Handwritten Text Recognition, Scene Text Recognition

Connectionist Temporal Classification (CTC) is a training criterion designed for sequence labelling problems where the alignment between the inputs and the target labels is unknown. One of the key steps is to add a blank symbol to the target vocabulary. However, CTC tends to output spiky distributions since it prefers to output blank symbol most of the time. These spiky distributions show inferior alignments and the non-blank symbols are not learned sufficiently. To remedy this, we propose variational CTC (Var-CTC) to enhance the learning of non-blank symbols. The proposed Var-CTC converts the output distribution of vanilla CTC with hierarchy distribution. It first learns the approximated posterior distribution of blank to determine whether to output a specific non-blank symbol or not. Then it learns the alignment between non-blank symbols and input sequence. Experiments on scene text recognition and offline handwritten text recognition show Var-CTC achieves better alignments. Besides, with the enhanced learning of non-blank symbols, the confidence scores of model outputs are more discriminative. Compared with the vanilla CTC, the proposed Var-CTC can improve the recall performance by a large margin when the models maintain the same level of precision.
End-to-end Dynamic Matching Network for Multi-view Multi-person 3d Pose Estimation
Authors: Congzhentao Huang, Shuai Jiang, Yang Li, Ziyue Zhang, Jason Traish, Chen Deng, Sam Ferguson, Richard Yi Da Xu
Date: 2020-08-01 00:00:00
Proceeding: ECCV 2020 8
Tasks: 3D Generation, 3D Human Pose Estimation, 3D Multi-Person Pose Estimation, 3D Pose Estimation, Pose Estimation

As an important computer vision task, 3d human pose estimation in a multi-camera, multi-person setting has received widespread attention and many interesting applications have been derived from it. Traditional approaches use a 3d pictorial structure model to handle this task. However, these models suffer from high computation costs and result in low accuracy in joint detection. Recently, especially since the introduction of Deep Neural Networks, one popular approach is to build a pipeline that involves three separate steps: (1) 2d skeleton detection in each camera view, (2) identification of matched 2d skeletons and (3) estimation of the 3d poses. Many existing works operate by feeding the 2d images and camera parameters through the three modules in a cascade fashion. However, all three operations can be highly correlated. For example, the 3d generation results may affect the results of detection in step 1, as does the matching algorithm in step 2. To address this phenomenon, we propose a novel end-to-end training scheme that brings the three separate modules into a single model. However, one outstanding problem of doing so is that the matching algorithm in step 2 appears to disjoint the pipeline. Therefore, we take our inspiration from the recent success in Capsule Networks, in which its Dynamic Routing step is also disjointed, but plays a crucial role in deciding how gradients are flowed from the upper to the lower layers. Similarly, a dynamic matching module in our work also decides the paths in which gradients flow from step 3 to step 1. Furthermore, as a large number of cameras are present, the existing matching algorithm either fails to deliver a robust performance or can be very inefficient. Thus, we additionally propose a novel matching algorithm that can match 2d poses from multiple views efficiently. The algorithm is robust and able to deal with situations of incomplete and false 2d detection as well.
Orderly Disorder in Point Cloud Domain
Authors: Morteza Ghahremani, Bernard Tiddeman, Yonghuai Liu, and Ardhendu Behera
Date: 2020-08-01 00:00:00
Proceeding: ECCV 2020 8

In the real world, out-of-distribution samples, noise and distortions exist in test data. Existing deep networks developed for point cloud data analysis are prone to overfitting and a partial change in test data leads to unpredictable behaviour of the networks. In this paper, we propose a smart yet simple deep network for analysis of 3D models using `orderly disorder' theory. Orderly disorder is a way of describing the complex structure of disorders within complex systems. Our method extracts the deep patterns inside a 3D object via creating a dynamic link to seek the most stable patterns and at once, throws away the unstable ones. Patterns are more robust to changes in data distribution, especially those that appear in the top layers. Features are extracted via an innovative cloning decomposition technique and then linked to each other to form stable complex patterns. Our model alleviates the vanishing-gradient problem, strengthens dynamic link propagation and substantially reduces the number of parameters. Extensive experiments on challenging benchmark datasets verify the superiority of our light network on the segmentation and classification tasks, especially in the presence of noise wherein our network's performance drops less than 10\% while the state-of-the-art networks fail to work.
Accurate Reconstruction of Oriented 3D Points using Affine Correspondences
Authors: Carolina Raposo, Joao P. Barreto
Date: 2020-08-01 00:00:00
Proceeding: ECCV 2020 8

Affine correspondences (ACs) have been an active topic of research, namely for the recovery of surface normals. However, current solutions still suffer from the fact that even state-of-the-art affine feature detectors are inaccurate, and ACs are often contaminated by large levels of noise, yielding poor surface normals. This article provides new formulations for achieving epipolar geometry-consistent ACs, that, besides leading to linear solvers that are up to 30$ imes$ faster than the state-of-the-art alternatives, allow for a fast refinement scheme that significantly improves the quality of the noisy ACs. In addition, a tracker that automatically enforces the epipolar geometry is proposed, with experiments showing that it significantly outperforms competing methods in situations of low texture. This opens the way to application domains where the scenes are typically low textured, such as during arthroscopic procedures.
360(o) Camera Alignment via Segmentation
Authors: Benjamin Davidson, Mohsan S. Alvi, João F. Henriques
Date: 2020-08-01 00:00:00
Proceeding: ECCV 2020 8
Tasks: Segmentation, valid

Panoramic 360º images taken under unconstrained conditions present a significant challenge to current state-of-the-art recognition pipelines, since the assumption of a mostly upright camera is no longer valid. In this work, we investigate how to solve this problem by fusing purely geometric cues, such as apparent vanishing points, with learned semantic cues, such as the expectation that some visual elements (e.g. doors) have a natural upright position. We train a deep neural network to leverage these cues to segment the image-space endpoints of an imagined “vertical axis”, which is orthogonal to the ground plane of a scene, thus levelling the camera. We show that our segmentation-based strategy significantly increases performance, reducing errors by half, compared to the current state-of-the-art on two datasets of 360º imagery. We also demonstrate the importance of 360º camera levelling by analysing its impact on downstream tasks, finding that incorrect levelling severely degrades the performance of real-world computer vision pipelines.
A Novel Line Integral Transform for 2D Affine-Invariant Shape Retrieval
Authors: Bin Wang, Yongsheng Gao
Date: 2020-08-01 00:00:00
Proceeding: ECCV 2020 8
Tasks: Retrieval

Radon transform is a popular mathematical tool for shape analysis. However, it cannot handle affine deformation. Although its extended version, trace transform, allow us to construct affine invariants, they are less informative and computational expensive due to the loss of spatial relationship between trace lines and the extensive repeated calculation of transform. To address this issue, a novel line integral transform is proposed. We first use binding line pairs that have the desirable property of affine preserving as a reference frame to rewrite the diametrical dimension parameters of the lines in a relative manner which make them independent on affine transform. Along polar angle dimension of the line parameters, a moment-based normalization is then conducted to degrade the affine transform to similarity transform which can be easily normalized by Fourier transform. The proposed transform is not only invariant to affine transform, but also preserves the spatial relationship between line integrals which make it very informative. Another advantage of the proposed transform is that it is more efficient than the trace transform. Conducting it one time can allow us to achieve a 2D matrix of affine invariants. While conducting the trace transform once only generates a single feature and multiple trace transforms of different functionals are needed to derive more to make the descriptors informative. The effectiveness of the proposed transform has been validated on two types of standard shape test cases, affinely distorted contour shape dataset and region shape dataset, respectively.
Guided Semantic Flow
Authors: Sangryul Jeon, Dongbo Min, Seungryong Kim, Jihwan Choe, Kwanghoon Sohn
Date: 2020-08-01 00:00:00
Proceeding: ECCV 2020 8
Tasks: Semantic correspondence

Establishing dense semantic correspondences requires dealing with large geometric variations caused by the unconstrained setting of images. To address such severe matching ambiguities, we introduce a novel approach, called {guided semantic flow}, based on the key insight that sparse yet reliable matches can effectively capture non-rigid geometric variations, and these confident matches can guide adjacent pixels to have similar solution spaces, reducing the matching ambiguities significantly. We realize this idea with learning-based selection of confident matches from an initial set of all pairwise matching scores and their propagation by a new differentiable upsampling layer based on moving least square concept. We take advantage of the guidance from reliable matches to refine the matching hypotheses through Gaussian parametric model in the subsequent matching pipeline. With the proposed method, state-of-the-art performance is attained on several standard benchmarks for semantic correspondence.
Measuring the Importance of Temporal Features in Video Saliency
Authors: Matthias Tangemann, Matthias Kümmerer, Thomas S. A. Wallis, Matthias Bethge
Date: 2020-08-01 00:00:00
Proceeding: ECCV 2020 8

Where people look when watching videos is believed to be heavily influenced by temporal patterns. In this work, we test this assumption by quantifying to which extent gaze on recent video saliency benchmarks can be predicted by a static baseline model. On the recent LEDOV dataset, we find that at least 75% of the explainable information as defined by a gold standard model can be explained using static features. Our baseline model ``DeepGaze MR'' even outperforms state-of-the-art video saliency models, despite deliberately ignoring all temporal patterns. Visual inspection of our static baseline’s failure cases shows that clear temporal effects on human gaze placement exist, but are both rare in the dataset and not captured by any of the recent video saliency models. To focus the development of video saliency models on better capturing temporal effects we construct a meta-dataset consisting of those examples requiring temporal information.
Towards Reliable Evaluation of Algorithms for Road Network Reconstruction from Aerial Images
Authors: Leonardo Citraro, Mateusz Koziński, Pascal Fua
Date: 2020-08-01 00:00:00
Proceeding: ECCV 2020 8

Existing connectivity-oriented performance measures rank road delineation algorithms inconsistently, which makes it difficult to decide which one is best for a given application. We show that these inconsistencies stem from design flaws that make the metrics insensitive to whole classes of errors. This insensitivity is undesirable in metrics intended for capturing overall general quality of road reconstructions. In particular, the scores do not reflect the time needed for a human to fix the errors, because each one has to be fixed individually. To provide more reliable evaluation, we design three new metrics that are sensitive to all classes of errors. This sensitivity makes them more consistent even though they use very different approaches to comparing ground-truth and reconstructed road networks.We use both synthetic and real data to demonstrate this and advocate the use of these corrected metrics as a tool to gauge future progress.
Defense Against Adversarial Attacks via Controlling Gradient Leaking on Embedded Manifolds
Authors: Yueru Li, Shuyu Cheng, Hang Su, Jun Zhu
Date: 2020-08-01 00:00:00
Proceeding: ECCV 2020 8

Deep neural networks are vulnerable to adversarial attacks. Though various attempts have been made, it is still largely open to fully understand the existence of adversarial samples and thereby develop effective defense strategies. In this paper, we present a new perspective, namely gradient leaking hypothesis, to understand the existence of adversarial examples and to further motivate effective defense strategies. Specifically, we consider the low dimensional manifold structure of natural images, and empirically verify that the leakage of the gradient (w.r.t input) along the (approximately) perpendicular direction to the tangent space of data manifold is a reason for the vulnerability over adversarial attacks. Based on our investigation, we further present a new robust learning algorithm which encourages a larger gradient component in the tangent space of data manifold, suppressing the gradient leaking phenomenon consequently. Experiments on various tasks demonstrate the effectiveness of our algorithm despite its simplicity.
Stereo Event-based Particle Tracking Velocimetry for 3D Fluid Flow Reconstruction
Authors: Yuanhao Wang, Ramzi Idoughi, Wolfgang Heidrich
Date: 2020-08-01 00:00:00
Proceeding: ECCV 2020 8
Tasks: Stereo Matching

Existing Particle Imaging Velocimetry techniques require the use of high-speed cameras to reconstruct time-resolved fluid flows. These cameras provides high-resolution images at high frame rates, which generates bandwidth and memory issues. By capturing only changes in the brightness with a very low latency and at low data rate, event-based cameras have the ability to tackle such issues. In this paper, we present a new framework that retrieves dense 3D measurements of the fluid velocity field using a pair of event-based cameras. First, we track particles inside the two event sequences in order to estimate their 2D velocity in the two sequences of images. A stereo-matching step is then performed to retrieve their 3D positions. These intermediate outputs are incorporated into an optimization framework that also includes physically plausible regularizers, in order to retrieve the 3D velocity field. Extensive experiments on both simulated and real data demonstrate the efficacy of our approach.
Distance-Normalized Unified Representation for Monocular 3D Object Detection
Authors: Xuepeng Shi, Zhixiang Chen, Tae-Kyun Kim
Date: 2020-08-01 00:00:00
Proceeding: ECCV 2020 8
Tasks: 3D Object Detection, Autonomous Driving, Monocular 3D Object Detection, Object, object-detection, Object Detection

Monocular 3D object detection plays an important role in autonomous driving and still remains challenging. To achieve fast and accurate monocular 3D object detection, we introduce a single-stage and multi-scale framework to learn a unified representation for objects within different distance ranges, termed as UR3D. UR3D formulates different tasks of detection by exploiting the scale information, to reduce model capacity requirement and achieve accurate monocular 3D object detection. Besides, distance estimation is enhanced by a distance-guided NMS, which automatically selects candidate boxes with better distance estimates. In addition, an efficient fully convolutional cascaded point regression method is proposed to infer accurate locations of the projected 2D corners and centers of 3D boxes, which can be used to recover object physical size and orientation by a projection-consistency loss. Experimental results on the challenging KITTI autonomous driving dataset show that UR3D achieves accurate monocular 3D object detection with a compact architecture.