GenAI4ELab/papercli-papers huggingface dataset:
Handcrafted Outlier Detection Revisited
Authors: Luca Cavalli; Viktor Larsson; Martin Ralf Oswald; Torsten Sattler; Marc Pollefeys
Venue: ECCV 2020 (main)

Abstract: Local feature matching is a critical part of many computer vision pipelines, including among others Structure-from-Motion, SLAM, and Visual Localization. However, due to limitations in the descriptors, raw matches are often contaminated by a majority of outliers. As a result, outlier detection is a fundamental problem in computer vision and a wide range of approaches, from simple checks based on descriptor similarity to geometric verification, have been proposed over the last decades. In recent years, deep learning-based approaches to outlier detection have become popular. Unfortunately, the corresponding works rarely compare with strong classical baselines. In this paper we revisit handcrafted approaches to outlier filtering. Based on best practices, we propose a hierarchical pipeline for effective outlier detection as well as integrate novel ideas which in sum lead to an efficient and competitive approach to outlier rejection. We show that our approach, although not relying on learning, is more than competitive to both recent learned works as well as handcrafted approaches, both in terms of efficiency and effectiveness. The code is available at https://github.com/cavalli1234/AdaLAM}{https://github.com/cavalli1234/AdaLAM."
The Average Mixing Kernel Signature
Authors: Luca Cosmo; Giorgia Minello; Michael Bronstein; Luca Rossi; Andrea Torsello
Venue: ECCV 2020 (main)

Abstract: We introduce the Average Mixing Kernel Signature (AMKS), a novel signature for points on non-rigid three-dimensional shapes based on the average mixing kernel and continuous-time quantum walks. The average mixing kernel holds information on the average transition probabilities of a quantum walk between each pair of vertices of the mesh until a time $T$. We define the AMKS by decomposing the spectral contributions of the kernel into several bands, allowing us to limit the influence of noise-dominated high-frequency components and obtain a more descriptive signature. We also show through a perturbation theory analysis of the kernel that choosing a finite stopping time $T$ leads to noise and deformation robustness for the AMKS. We perform an extensive experimental evaluation on two widely used shape matching datasets under varying level of noise, showing that the AMKS outperforms two state-of-the-art descriptors, namely the Heat Kernel Signature (HKS) and the similarly quantum-walk based Wave Kernel Signature (WKS)."
BCNet: Learning Body and Cloth Shape from A Single Image
Authors: Boyi Jiang; Juyong Zhang; Yang Hong; Jinhao Luo; Ligang Liu; Hujun Bao
Venue: ECCV 2020 (main)

Abstract: In this paper, we consider the problem to automatically reconstruct garment and body shapes from a single near-front view RGB image. To this end, we propose a layered garment representation on top of SMPL and novelly make the skinning weight of garment independent of the body mesh, which significantly improves the expression ability of our garment model. Compared with existing methods, our method can support more garment categories and recover more accurate geometry. To train our model, we construct two large scale datasets with ground truth body and garment geometries as well as paired color images. Compared with single mesh or non-parametric representation, our method can achieve more flexible control with separate meshes, makes applications like re-pose, garment transfer, and garment texture mapping possible."
Self-supervised Keypoint Correspondences for Multi-Person Pose Estimation and Tracking in Videos
Authors: Umer Rafi; Andreas Doering; Bastian Leibe; Juergen Gall
Venue: ECCV 2020 (main)

Abstract: Video annotation is expensive and time consuming. Consequently, datasets for multi-person pose estimation and tracking are less diverse and have more sparse annotations compared to large scale image datasets for human pose estimation. This makes it challenging to learn deep learning based models for associating keypoints across frames that are robust to nuisance factors such as motion blur and occlusions for the task of multi-person pose tracking. %It is clear that leveraging large scale human pose annotations from single images benefits frame level human pose estimation in videos. This raises the question can large scale human pose annotations from single images benefit muti-person pose tracking. To address this issue, we propose an approach that relies on keypoint correspondences for associating persons in videos. Instead of training the network for estimating keypoint correspondences on video data, it is trained on a large scale image datasets for human pose estimation using self-supervision. Combined with a top-down framework for human pose estimation, we use keypoints correspondences to (i) recover missed pose detections (ii) associate pose detections across video frames. Our approach achieves state-of-the-art results for temporal pose estimation and multi-person pose tracking on the PosTrack $2017$ and PoseTrack $2018$ data sets. "
Interactive Multi-Dimension Modulation with Dynamic Controllable Residual Learning for Image Restoration
Authors: Jingwen He; Chao Dong; Yu Qiao
Venue: ECCV 2020 (main)

Abstract: Interactive image restoration aims to generate restored images by adjusting a controlling coefficient which determines the restoration level. Previous works are restricted in modulating image with a single coefficient. However, real images always contain multiple types of degradation, which cannot be well determined by one coefficient. To make a step forward, this paper presents a new problem setup, called multi-dimension (MD) modulation, which aims at modulating output effects across multiple degradation types and levels. Compared with the previous single-dimension (SD) modulation, the MD is setup to handle multiple degradations adaptively and relief unbalanced learning problem in different degradations. We also propose a deep architecture - CResMD with newly introduced controllable residual connections for multi-dimension modulation. Specifically, we add a controlling variable on the conventional residual connection to allow a weighted summation of input and residual. The values of these weights are generated by another condition network. We further propose a new data sampling strategy based on beta distribution to balance different degradation types and levels. With corrupted image and degradation information as inputs, the network can output the corresponding restored image. By tweaking the condition vector, users can control the output effects in MD space at test time. Extensive experiments demonstrate that the proposed CResMD achieve excellent performance on both SD and MD modulation tasks. Code is available at \url{https://github.com/hejingwenhejingwen/CResMD}."
Polysemy Deciphering Network for Human-Object Interaction Detection
Authors: Xubin Zhong; Changxing Ding; Xian Qu; Dacheng Tao
Venue: ECCV 2020 (main)

Abstract: Human-Object Interaction (HOI) detection is important in human-centric scene understanding. Existing works typically assume that the same verb in different HOI categories has similar visual characteristics, while ignoring the diverse semantic meanings of the verb. To address this issue, in this paper, we propose a novel Polysemy Deciphering Network (PD-Net), which decodes the visual polysemy of verbs for HOI detection in three ways. First, PD-Net augments human pose and spatial features for HOI detection using language priors, enabling the verb classifiers to receive language hints that reduce the intra-class variation of the same verb. Second, we introduce a novel Polysemy Attention Module (PAM) that guides PD-Net to make decisions based on more important feature types according to the language priors. Finally, the above two strategies are applied to two types of classifiers for verb recognition, i.e., object-shared and object-specific verb classifiers, whose combination further relieves the verb polysemy problem. By deciphering the visual polysemy of verbs, we achieve the best performance on both HICO-DET and V-COCO datasets. In particular, PD-Net outperforms state-of-the-art approaches by 3.81% mAP in the Known-Object evaluation mode of HICO-DET. Code of PD-Net will be released at https://github.com/MuchHair/PD-Net."
PODNet: Pooled Outputs Distillation for Small-Tasks Incremental Learning
Authors: Arthur Douillard; Matthieu Cord; Charles Ollion; Thomas Robert; Eduardo Valle
Venue: ECCV 2020 (main)

Abstract: Lifelong learning has attracted much attention, but existing works still struggle to fight catastrophic forgetting and accumulate knowledge over long stretches of incremental learning. In this work, we propose PODNet, a model inspired by representation learning. By carefully balancing the compromise between remembering the old classes and learning new ones, PODNet fights catastrophic forgetting, even over very long runs of small incremental tasks, a setting so far unexplored by current works. PODNet innovates on existing art with an efficient spatial-based distillation-loss applied throughout the model and a representation comprising multiple proxy vectors for each class. We validate those innovations thoroughly, comparing PODNet with three state-of-the-art models on three datasets: CIFAR100, ImageNet100, and ImageNet1000. Our results showcase a significant advantage of PODNet over existing art, with accuracy gains of 12.10, 6.51, and 2.85 percentage points, respectively."
Learning Graph-Convolutional Representations for Point Cloud Denoising
Authors: Francesca Pistilli; Giulia Fracastoro; Diego Valsesia; Enrico Magli
Venue: ECCV 2020 (main)

Abstract: Point clouds are an increasingly relevant data type but they are often corrupted by noise. We propose a deep neural network based on graph-convolutional layers that can elegantly deal with the permutation-invariance problem encountered by learning-based point cloud processing methods. The network is fully-convolutional and can build complex hierarchies of features by dynamically constructing neighborhood graphs from similarity among the high-dimensional feature representations of the points. When coupled with a loss promoting proximity to the ideal surface, the proposed approach significantly outperforms state-of-the-art methods on a variety of metrics. In particular, it is able to improve in terms of Chamfer measure and of quality of the surface normals that can be estimated from the denoised data. We also show that it is especially robust both at high noise levels and in presence of structured noise such as the one encountered in real LiDAR scans."
Semantic Line Detection Using Mirror Attention and Comparative Ranking and Matching
Authors: Dongkwon Jin; Jun-Tae Lee; Chang-Su Kim
Venue: ECCV 2020 (main)

Abstract: A novel algorithm to detect semantic lines is proposed in this paper. We develop three networks: detection network with mirror attention (D-Net) and comparative ranking and matching networks (R-Net and M-Net). D-Net extracts semantic lines by exploiting rich contextual information. To this end, we design the mirror attention module. Then, through pairwise comparisons of extracted semantic lines, we iteratively select the most semantic line and remove redundant ones overlapping with the selected one. For the pairwise comparisons, we develop R-Net and M-Net in the Siamese architecture. Experiments demonstrate that the proposed algorithm outperforms the conventional semantic line detector significantly. Moreover, we apply the proposed algorithm to detect two important kinds of semantic lines successfully: dominant parallel lines and reflection symmetry axes."
A Differentiable Recurrent Surface for Asynchronous Event-Based Data
Authors: Marco Cannici; Marco Ciccone; Andrea Romanoni; Matteo Matteucci
Venue: ECCV 2020 (main)

Abstract: Dynamic Vision Sensors (DVSs) asynchronously stream events in correspondence of pixels subject to brightness changes. Differently from classic vision devices, they produce a sparse representation of the scene. Therefore, to apply standard computer vision algorithms, events need to be integrated into a frame or event-surface. This is usually attained through hand-crafted grids that reconstruct the frame using ad-hoc heuristics. In this paper, we propose Matrix-LSTM, a grid of Long Short-Term Memory (LSTM) cells that efficiently process events and learn end-to-end task-dependent event-surfaces. Compared to existing reconstruction approaches, our learned event-surface shows good flexibility and expressiveness on optical flow estimation on the MVSEC benchmark and it improves the state-of-the-art of event-based object classification on the N-Cars dataset."
Fine-Grained Visual Classification via Progressive Multi-Granularity Training of Jigsaw Patches
Authors: Ruoyi Du; Dongliang Chang; Ayan Kumar Bhunia; Jiyang Xie; Zhanyu Ma; Yi-Zhe Song; Jun Guo
Venue: ECCV 2020 (main)

Abstract: Fine-grained visual classiﬁcation (FGVC) is much more challenging than traditional classiﬁcation tasks due to the inherently subtle intra-class object variations. Recent works mainly tackle this problem by focusing on how to locate the most discriminative parts, more complementary parts, and parts of various granularities. However, less effort has been placed to which granularities are the most discriminative and how to fuse information cross multi-granularity. In this work, we propose a novel framework for fine-grained visual classiﬁcation to tackle these problems. In particular, we propose: (i) a novel progressive training strategy that adds new layers in each training step to exploit information based on the smaller granularity information found at the last step and the previous stage. (ii) a simple jigsaw puzzle generator to form images contain information of different granularity levels. We obtain state-of-the-art performances on several standard FGVC benchmark datasets, where the proposed method consistently outperforms existing methods or delivers competitive results. Code is provided as part of supplementary material, and will be publicly released upon acceptance."
LiteFlowNet3: Resolving Correspondence Ambiguity for More Accurate Optical Flow Estimation
Authors: Tak-Wai Hui; Chen Change Loy
Venue: ECCV 2020 (main)

Abstract: Deep learning approaches have achieved great success in addressing the problem of optical flow estimation. The keys to success lie in the use of cost volume and coarse-to-fine flow inference. However, the matching problem becomes ill-posed when partially occluded or homogeneous regions exist in images. This causes a cost volume to contain outliers and affects the flow decoding from it. Besides, the coarse-to-fine flow inference demands an accurate flow initialization. Ambiguous correspondence yields erroneous flow fields and affects the flow inferences in subsequent levels. In this paper, we introduce LiteFlowNet3, a deep network consisting of two specialized modules, to address the above challenges. (1) We ameliorate the issue of outliers in the cost volume by amending each cost vector through an adaptive modulation prior to the flow decoding. (2) We further improve the flow accuracy by exploring local flow consistency. To this end, each inaccurate optical flow is replaced with an accurate one from a nearby position through a novel warping of the flow field. LiteFlowNet3 not only achieves promising results on public benchmarks but also has a small model size and a fast runtime."
Microscopy Image Restoration with Deep Wiener-Kolmogorov Filters
Authors: Valeriya Pronina; Filippos Kokkinos; Dmitry V. Dylov; Stamatios Lefkimmiatis
Venue: ECCV 2020 (main)

Abstract: Microscopy is a powerful visualization tool in biology, enabling the study of cells, tissues, and the fundamental biological processes; yet, the observed images typically suffer from blur and background noise. In this work, we propose a unifying framework of algorithms for Gaussian image deblurring and denoising. These algorithms are based on deep learning techniques for the design of learnable regularizers integrated into the Wiener-Kolmogorov filter. Our extensive experimentation line showcases that the proposed approach achieves a superior quality of image reconstruction and surpasses the solutions that rely either on deep learning or on optimization schemes alone. Augmented with the variance stabilizing transformation, the proposed reconstruction pipeline can also be successfully applied to the problem of Poisson image deblurring, surpassing the state-of-the-art methods. Moreover, several variants of the proposed framework demonstrate competitive performance at low computational complexity, which is of high importance for real-time imaging applications."
ScanRefer: 3D Object Localization in RGB-D Scans using Natural Language
Authors: Dave Zhenyu Chen; Angel X. Chang; Matthias Nießner
Venue: ECCV 2020 (main)

Abstract: We introduce the new task of 3D object localization in RGB-D scans using natural language descriptions. As input, we assume a point cloud of a scanned 3D scene along with a free-form description of a specified target object. To address this task, we propose ScanRefer, where the core idea is to learn a fused descriptor from 3D object proposals and encoded sentence embeddings. This learned descriptor then correlates the language expressions with the underlying geometric features of the 3D scan and facilitates the regression of the 3D bounding box of the target object. In order to train and benchmark our method, we introduce a new ScanRefer dataset, containing 46,173 descriptions of 9,943 objects from 703 ScanNet scenes. ScanRefer is the first large-scale effort to perform object localization via natural language expression directly in 3D."
JSENet: Joint Semantic Segmentation and Edge Detection Network for 3D Point Clouds
Authors: Zeyu Hu; Mingmin Zhen; Xuyang Bai; Hongbo Fu; Chiew-lan Tai
Venue: ECCV 2020 (main)

Abstract: Semantic segmentation and semantic edge detection can be seen as two dual problems with close relationships in computer vision. Despite the fast evolution of learning-based 3D semantic segmentation methods, little attention has been drawn to the learning of 3D semantic edge detectors, even less to a joint learning method for the two tasks. In this paper, we tackle the 3D semantic edge detection task for the first time and present a new two-stream fully-convolutional network that jointly performs the two tasks. In particular, we design a joint refinement module that explicitly wires region information and edge information to improve the performances of both tasks. Further, we propose a novel loss function that encourages the network to produce semantic segmentation results with better boundaries. Extensive evaluations on S3DIS and ScanNet datasets show that our method achieves on par or better performance than the state-of-the-art methods for semantic segmentation and outperforms the baseline methods for semantic edge detection. "
Motion-Excited Sampler: Video Adversarial Attack with Sparked Prior
Authors: Hu Zhang; Linchao Zhu; Yi Zhu; Yi Yang
Venue: ECCV 2020 (main)

Abstract: Deep neural networks are known to be susceptible to adversarial noise, which is tiny and imperceptible perturbation. Most of previous works on adversarial attack mainly focus on image models, while the vulnerability of video models is less explored. In this paper, we aim to attack video models by utilizing intrinsic movement pattern and regional relative motion among video frames. We propose an effective motion-excited sampler to obtain motion-aware noise prior, which we term as sparked prior. Our sparked prior underlines frame correlations and utilizes video dynamics via relative motion. By using the sparked prior in gradient estimation, we can successfully attack a variety of video classification models with fewer number of queries. Extensive experimental results on four benchmark datasets validate the efficacy of our proposed method. "
An Inference Algorithm for Multi-Label MRF-MAP Problems with Clique Size 100
Authors: Ishant Shanu; Siddhant Bharti; Chetan Arora; S. N. Maheshwari
Venue: ECCV 2020 (main)

Abstract: In this paper, we propose an algorithm for optimal solutions to submodular higher-order multi-label MRF-MAP energy functions which can handle practical computer vision problems with up to 16 labels and cliques of size 100. The algorithm uses a transformation which transforms a multi-label problem to a 2-label problem on a much larger clique. Earlier algorithms based on this transformation could not handle problems larger than 16 labels on cliques of size 4. The proposed algorithm optimizes the resultant 2-label problem using the submodular polyhedron based Min Norm Point algorithm. The task is challenging because the state space of the transformed problem has a very large number of invalid states. For polyhedral based algorithms the presence of invalid states poses a challenge as apart from numerical instability, the transformation also increases the dimension of the polyhedral space making the straightforward use of known algorithms impractical. The approach reported in this paper allows us to bypass the large costs associated with invalid configurations, resulting in a stable, practical, optimal and efficient inference algorithm that, in our experiments, gives high-quality outputs on problems like pixel-wise object segmentation and stereo matching."
Dual Refinement Underwater Object Detection Network
Authors: Baojie Fan; Wei Chen; Yang Cong; Jiandong Tian
Venue: ECCV 2020 (main)

Abstract: Due to the complex underwater environment, underwater imaging often encounters some problems such as blur, scale variation, color shift, and texture distortion. Generic detection algorithms can not work well when we use them directly in the underwater scene. To address these problems, we propose an underwater detection framework with feature enhancement and anchor refinement. It has a composite connection backbone to boost the feature representation and introduces a receptive field augmentation module to exploit multi-scale contextual features. The developed underwater object detection framework also provides a prediction refinement scheme according to six prediction layers, it can refine multi-scale features to better align with anchors by learning from offsets, which solve the problem of sample imbalance to a certain extent. We also construct a new underwater detection dataset, denoted as UWD, which has more than 10,000 train-val and test underwater images. The extensive experiments on PASCAL VOC and UWD demonstrate the favorable performance of the proposed underwater detection framework against the states-of-the-arts methods in terms of accuracy and robustness. Source code and models are available at: https://github.com/Peterchen111/FERNet."
Multiple Sound Sources Localization from Coarse to Fine
Authors: Rui Qian; Di Hu; Heinrich Dinkel; Mengyue Wu; Ning Xu; Weiyao Lin
Venue: ECCV 2020 (main)

Abstract: How to visually localize multiple sound sources in unconstrained videos is a formidable problem, especially when lack of the pairwise sound-object annotations. To solve this problem, we develop a two-stage audiovisual learning framework that disentangles audio and visual representations of different categories from complex scenes, then performs cross-modal feature alignment in a coarse-to-fine manner. Our model achieves state-of-the-art results on public dataset of localization, as well as considerable performance on multi-source sound localization in complex scenes. We then employ the localization results for sound separation and obtain comparable performance to existing methods. These outcomes demonstrate our model's ability in effectively aligning sounds with specific visual sources."