GenAI4ELab/papercli-papers huggingface dataset:
Explanation-based Weakly-supervised Learning of Visual Relations with Graph Networks
Authors: Federico Baldassarre; Kevin Smith; Josephine Sullivan; Hossein Azizpour
Venue: ECCV 2020 (main)

Abstract: Visual relationship detection is fundamental for holistic image understanding. However, the localization and classification of (subject, predicate, object) triplets remain challenging tasks, due to the combinatorial explosion of possible relationships, their long-tailed distribution in natural images, and an expensive annotation process. This paper introduces a novel weakly-supervised method for visual relationship detection that relies on minimal image-level predicate labels. A graph neural network is trained to classify predicates in images from a graph representation of detected objects, implicitly encoding an inductive bias for pairwise relations. We then frame relationship detection as the explanation of such a predicate classifier, i.e. we obtain a complete relation by recovering the subject and object of a predicted predicate. We present results comparable to recent fully- and weakly-supervised methods on three diverse and challenging datasets: HICO-DET for human-object interaction, Visual Relationship Detection for generic object-to-object relations, and UnRel for unusual triplets; demonstrating robustness to non-comprehensive annotations and good few-shot generalization."
Guided Semantic Flow
Authors: Sangryul Jeon; Dongbo Min; Seungryong Kim; Jihwan Choe; Kwanghoon Sohn
Venue: ECCV 2020 (main)

Abstract: 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."
Document Structure Extraction using Prior based High Resolution Hierarchical Semantic Segmentation
Authors: Mausoom Sarkar; Milan Aggarwal; Arneh Jain; Hiresh Gupta; Balaji Krishnamurthy
Venue: ECCV 2020 (main)

Abstract: Structure extraction from document images has been a long-standing research topic due to its high impact on a wide range of practical applications. In this paper, we share our findings on employing a hierarchical semantic segmentation network for this task of structure extraction. We propose a prior based deep hierarchical CNN network architecture that enables document structure extraction using very high resolution(1800 x 1000) images. We divide the document image into overlapping horizontal strips such that the network segments a strip and uses its prediction mask as prior for predicting the segmentation of the subsequent strip. We perform experiments establishing the effectiveness of our strip based network architecture through ablation methods and comparison with low-resolution variations. Further, to demonstrate our network's capabilities, we train it on only one type of documents (Forms) and achieve state-of-the-art results over other general document datasets. We introduce our new human-annotated forms dataset and show that our method significantly outperforms different segmentation baselines on this dataset in extracting hierarchical structures.Our method is currently being used in Adobe's AEM Forms for automated conversion of paper and PDF forms to modern HTML based forms."
Measuring the Importance of Temporal Features in Video Saliency
Authors: Matthias Tangemann; Matthias Kümmerer; Thomas S.A. Wallis; Matthias Bethge
Venue: ECCV 2020 (main)

Abstract: 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."
Searching Efficient 3D Architectures with Sparse Point-Voxel Convolution
Authors: Haotian Tang; Zhijian Liu; Shengyu Zhao; Yujun Lin; Ji Lin; Hanrui Wang; Song Han
Venue: ECCV 2020 (main)

Abstract: Self-driving cars need to understand 3D scenes efficiently and accurately in order to drive safely. Given the limited hardware resources, existing 3D perception models are not able to recognize small instances (e.g., pedestrians, cyclists) very well due to the low-resolution voxelization and aggressive downsampling. To this end, we propose Sparse Point-Voxel Convolution (SPVConv), a lightweight 3D module that equips the vanilla Sparse Convolution with the high-resolution point-based branch. With negligible overhead, this point-based branch is able to preserve the fine details even from large outdoor scenes. To explore the spectrum of efficient 3D models, we first define a flexible architecture design space based on SPVConv, and we then present 3D Neural Architecture Search (3D-NAS) to search the optimal network architecture over this diverse design space efficiently and effectively. Experimental results validate that the resulting SPVNAS model is fast and accurate: it outperforms the state-of-the-art MinkowskiNet by 3.3%, ranking 1st on the competitive SemanticKITTI leaderboard. It also achieves 8-24× computation reduction and 3-35× measured speedup over MinkowskiNet and KPConv with higher accuracy. Finally, we transfer our method to 3D object detection, and it achieves consistent improvements over the one-stage detection baseline on KITTI."
Towards Reliable Evaluation of Algorithms for Road Network Reconstruction from Aerial Images
Authors: Leonardo Citraro; Mateusz Koziński; Pascal Fua
Venue: ECCV 2020 (main)

Abstract: 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."
Online Continual Learning under Extreme Memory Constraints
Authors: Enrico Fini; Stéphane Lathuilière; Enver Sangineto; Moin Nabi; Elisa Ricci
Venue: ECCV 2020 (main)

Abstract: Continual Learning (CL) aims to develop agents emulating the human ability to sequentially learn new tasks while being able to retain knowledge obtained from past experiences. In this paper, we introduce the novel problem of Memory-Constrained Online Continual Learning (MC-OCL) which imposes strict constraints on the memory overhead that a possible algorithm can use to avoid catastrophic forgetting. As most, if not all, previous CL methods violate these constraints, we propose an algorithmic solution to MC-OCL: Batch-level Distillation (BLD), a regularization-based CL approach which effectively balances stability and plasticity in order to learn from data streams, while preserving the ability to solve old tasks through distillation. Our extensive experimental evaluation, conducted on three publicly available benchmarks, empirically demonstrates that our approach successfully addresses the MC-OCL problem and achieves comparable accuracy to prior distillation methods requiring higher memory overhead."
Learning to Cluster under Domain Shift
Authors: Willi Menapace; Stéphane Lathuilière; Elisa Ricci
Venue: ECCV 2020 (main)

Abstract: While unsupervised domain adaptation methods based on deep architectures have achieved remarkable success in many computer vision tasks, they rely on a strong assumption, i.e. labeled source data must be available. In this work we overcome this assumption and we address the problem of transferring knowledge from a source to a target domain when both source and target data have no annotations. Inspired by recent works on deep clustering, our approach leverages information from data gathered from multiple source domains to build a domain-agnostic clustering model which is then refined at inference time when target data become available. Specifically, at training time we propose to optimize a novel information-theoretic loss which, coupled with domain-alignment layers, ensures that our model learns to correctly discover semantic labels while discarding domain-specific features. Importantly, our architecture design ensures that at inference time the resulting source model can be effectively adapted to the target domain without having access to source data, thanks to feature alignment and self-supervision. We evaluate the proposed approach in a variety of settings, considering several domain adaptation benchmarks and we show that our method is able to automatically discover relevant semantic information even in presence of few target samples and yields state-of-the-art results on multiple domain adaptation benchmarks."
Defense Against Adversarial Attacks via Controlling Gradient Leaking on Embedded Manifolds
Authors: Yueru Li; Shuyu Cheng; Hang Su; Jun Zhu
Venue: ECCV 2020 (main)

Abstract: 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."
Improving Optical Flow on a Pyramid Level
Authors: Markus Hofinger; Samuel Rota Bulò; Lorenzo Porzi; Arno Knapitsch; Thomas Pock; Peter Kontschieder
Venue: ECCV 2020 (main)

Abstract: In this work we review the coarse-to-fine spatial feature pyramid concept, which is used in state-of-the-art optical flow estimation networks to make exploration of the pixel flow search space computationally tractable and efficient. Within an individual pyramid level, we improve the cost volume construction process by departing from a warping- to a sampling-based strategy, which avoids ghosting and hence enables us to better preserve fine flow details. We further amplify the positive effects through a level-specific, loss max-pooling strategy that adaptively shifts the focus of the learning process on under-performing predictions. Our second contribution revises the gradient flow across pyramid levels. The typical operations performed at each pyramid level can lead to noisy, or even contradicting gradients across levels. We show and discuss how properly blocking some of these gradient components leads to improved convergence and ultimately better performance. Finally, we introduce a distillation concept to counteract the issue of catastrophic forgetting and thus preserving knowledge over models sequentially trained on multiple datasets. Our findings are conceptually simple and easy to implement, yet result in compelling improvements on relevant error measures that we demonstrate via exhaustive ablations on datasets like Flying Chairs2, Flying Things, Sintel and KITTI. We establish new state-of-the-art results on the challenging Sintel and KITTI 2012 test datasets, and even show the portability of our findings to different optical flow and depth from stereo approaches."
Procrustean Regression Networks: Learning 3D Structure of Non-Rigid Objects from 2D Annotations
Authors: Sungheon Park; Minsik Lee; Nojun Kwak
Venue: ECCV 2020 (main)

Abstract: We propose a novel framework for training neural networks which is capable of learning 3D information of non-rigid objects when only 2D annotations are available as ground truths. Recently, there have been some approaches that incorporate the problem setting of non-rigid structure-from-motion (NRSfM) into deep learning to learn 3D structure reconstruction. The most important difficulty of NRSfM is to estimate both the rotation and deformation at the same time, and previous works handle this by regressing both of them. In this paper, we resolve this difficulty by proposing a loss function wherein the suitable rotation is automatically determined. Trained with the cost function consisting of the reprojection error and the low-rank term of aligned shapes, the network learns the 3D structures of such objects as human skeletons and faces during the training, whereas the testing is done in a single-frame basis. The proposed method can handle inputs with missing entries and experimental results validate that the proposed framework shows superior reconstruction performance to the state-of-the-art method on the Human 3.6M, 300-VW, and SURREAL dataset, even though the underlying network structure is very simple."
Learning to Learn Parameterized Classification Networks for Scalable Input Images
Authors: Duo Li; Anbang Yao; Qifeng Chen
Venue: ECCV 2020 (main)

Abstract: Convolutional Neural Networks (CNNs) do not have a predictable recognition behavior with respect to the input resolution change. This prevents the feasibility of deployment on different input image resolutions for a specific model. To achieve efficient and flexible image classification at runtime, we employ meta learners to generate convolutional weights of main networks for various input scales and maintain privatized Batch Normalization layers per scale. For improved training performance, we further utilize knowledge distillation on the fly over model predictions based on different input resolutions. The learned meta network could dynamically parameterize main networks to act on input images of arbitrary size with consistently better accuracy compared to individually trained models. Extensive experiments on the ImageNet demonstrate that our method achieves an improved accuracy-efficiency trade-off during the adaptive inference process. By switching executable input resolutions, our method could satisfy the requirement of fast adaption in different resource-constrained environments. Code and models are available at https://github.com/d-li14/SAN"
Stereo Event-based Particle Tracking Velocimetry for 3D Fluid Flow Reconstruction
Authors: Yuanhao Wang; Ramzi Idoughi; Wolfgang Heidrich
Venue: ECCV 2020 (main)

Abstract: 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."
Simplicial Complex based Point Correspondence between Images warped onto Manifolds
Authors: Charu Sharma; Manohar Kaul
Venue: ECCV 2020 (main)

Abstract: Recent increase in the availability of warped images projected onto a manifold (e.g., omnidirectional spherical images), coupled with the success of higher-order assignment methods, has sparked an interest in the search for improved higher-order matching algorithms on warped images due to projection. Although currently, several existing methods “flatten"" such 3D images to use planar graph / hypergraph matching methods, they still suffer from severe distortions and other undesired artifacts, which result in inaccurate matching. Alternatively, current planar methods cannot be trivially extended to effectively match points on images warped onto manifolds. Hence, matching on these warped images persists as a formidable challenge. In this paper, we pose the assignment problem as finding a bijective map between two graph induced simplicial complexes, which are higher-order analogues of graphs. We propose a constrained quadratic assignment problem (QAP) that matches each p-skeleton of the simplicial complexes, iterating from the highest to the lowest dimension. The accuracy and robustness of our approach are illustrated on both synthetic and real-world spherical / warped (projected) images with known ground-truth correspondences. We significantly outperform existing state-of-the-art spherical matching methods on a diverse set of datasets."
Representation Learning on Visual-Symbolic Graphs for Video Understanding
Authors: Effrosyni Mavroudi; Benjamín Béjar Haro; René Vidal
Venue: ECCV 2020 (main)

Abstract: Events in natural videos typically arise from spatio-temporal interactions between actors and objects and involve multiple co-occurring activities and object classes. To capture this rich visual and semantic context, we propose using two graphs:(1) an attributed spatio-temporal visual graph whose nodes correspond to actors and objects and whose edges encode different types of interactions, and (2) a symbolic graph that models semantic relationships. We further propose a graph neural network for refining the representations of actors, objects and their interactions on the resulting hybrid graph. Our framework goes beyond current approaches that assume nodes and edges of the same type, operate on a fixed graph structure and do not use a symbolic graph. In particular, our framework: a) has specialized attention-based aggregation functions for different node and edge types; b) uses visual edge features; c) integrates visual evidence with label relationships; and d) performs global reasoning in the semantic space. Experiments on challenging video understanding tasks, such as temporal action localization on the Charades dataset, show that the proposed method leads to state-of-the-art performance."
Distance-Normalized Unified Representation for Monocular 3D Object Detection
Authors: Xuepeng Shi; Zhixiang Chen; Tae-Kyun Kim
Venue: ECCV 2020 (main)

Abstract: 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."
Sequential Deformation for Accurate Scene Text Detection
Authors: Shanyu Xiao; Liangrui Peng; Ruijie Yan; Keyu An; Gang Yao; Jaesik Min
Venue: ECCV 2020 (main)

Abstract: Scene text detection has been significantly advanced over recent years, especially after the emergence of deep neural network. However, due to high diversity of scene texts in scale, orientation, shape and aspect ratio, as well as the inherent limitation of convolutional neural network for geometric transformations, to achieve accurate scene text detection is still an open problem. In this paper, we propose a novel sequential deformation method to effectively model the line-shape of scene text. An auxiliary character counting supervision is further introduced to guide the sequential offset prediction. The whole network can be easily optimized through an end-to-end multi-task manner. Extensive experiments are conducted on public scene text detection datasets including ICDAR 2017 MLT, ICDAR 2015, Total-text and SCUT-CTW1500. The experimental results demonstrate that the proposed method has outperformed previous state-of-the-art methods."
Where to Explore Next? ExHistCNN for History-aware Autonomous 3D Exploration
Authors: Yiming Wang; Alessio Del Bue
Venue: ECCV 2020 (main)

Abstract: In this work we address the problem of autonomous 3D exploration of an unknown indoor environment using a depth camera. We cast the problem as the estimation of the Next Best View (NBV) that maximises the coverage of the unknown area. We do this by re-formulating NBV estimation as a classification problem and we propose a novel learning-based metric that encodes both, the current 3D observation (a depth frame) and the history of the ongoing reconstruction. One of the major contributions of this work is about introducing a new representation for the 3D reconstruction history as an auxiliary utility map which is efficiently coupled with the current depth observation. With both pieces of information, we train a light-weight CNN, named ExHistCNN, that estimates the NBV as a set of directions towards which the depth sensor finds most unexplored areas. We perform extensive evaluation on both synthetic and real room scans demonstrating that the proposed ExHistCNN is able to approach the exploration performance of an oracle using the complete knowledge of the 3D environment."