GenAI4ELab/papercli-papers huggingface dataset:
Attention Guided Anomaly Localization in Images
Authors: Shashanka Venkataramanan; Kuan-Chuan Peng; Rajat Vikram Singh; Abhijit Mahalanobis
Venue: ECCV 2020 (main)

Abstract: Anomaly localization is an important problem in computer vision which involves localizing anomalous regions within images with applications in industrial inspection, surveillance, and medical imaging. This task is challenging due to the small sample size and pixel coverage of the anomaly in real-world scenarios. Most prior works need to use anomalous training images to compute a class-specific threshold to localize anomalies. Without the need of anomalous training images, we propose Convolutional Adversarial Variational autoencoder with Guided Attention (CAVGA), which localizes the anomaly with a convolutional latent variable to preserve the spatial information. In the unsupervised setting, we propose an attention expansion loss where we encourage CAVGA to focus on all normal regions in the image. Furthermore, in the weakly-supervised setting we propose a complementary guided attention loss, where we encourage the attention map to focus on all normal regions while minimizing the attention map corresponding to anomalous regions in the image. CAVGA outperforms the state-of-the-art (SOTA) anomaly localization methods on MVTec Anomaly Detection (MVTAD), modified ShanghaiTech Campus (mSTC) and Large-scale Attention based Glaucoma (LAG) datasets in the unsupervised setting and when using only 2% anomalous images in the weakly-supervised setting. CAVGA also outperforms SOTA anomaly detection methods on the MNIST, CIFAR-10,Fashion-MNIST, MVTAD, mSTC and LAG datasets."
Self-supervised Video Representation Learning by Pace Prediction
Authors: Jiangliu Wang; Jianbo Jiao; Yun-Hui Liu
Venue: ECCV 2020 (main)

Abstract: This paper addresses the problem of self-supervised video representation learning from a new perspective -- by video pace prediction. It stems from the observation that human visual system is sensitive to video pace, g, slow motion, a widely used technique in film making. Specifically, given a video played in natural pace, we randomly sample training clips in different paces and ask a neural network to identify the pace for each video clip. The assumption here is that the network can only succeed in such a pace reasoning task when it understands the underlying video content and learns representative spatio-temporal features. In addition, we further introduce contrastive learning to push the model towards discriminating different paces by maximizing the agreement on similar video content. To validate the effectiveness of the proposed method, we conduct extensive experiments on action recognition and video retrieval tasks with several alternative network architectures. Experimental evaluations show that our approach achieves state-of-the-art performance for self-supervised video representation learning across different network architectures and different benchmarks. The code and pre-trained models are available at \url{https://github.com/laura-wang/video-pace}.\keywords{Self-supervised learning nd Video representation nd Pace.}"
Full-Body Awareness from Partial Observations
Authors: Chris Rockwell; David F. Fouhey
Venue: ECCV 2020 (main)

Abstract: There has been great progress in human 3D mesh recovery and great interest in learning about the world from consumer video data. Unfortunately current methods for 3D human mesh recovery work rather poorly on consumer video data, since on the Internet, unusual camera viewpoints and aggressive truncations are the norm rather than a rarity. We study this problem and make a number of contributions to address it: (i) we propose a simple but highly effective self-training framework that adapts human 3D mesh recovery systems to consumer videos and demonstrate its application to two recent systems; (ii) we introduce evaluation protocols and keypoint annotations for 13K frames across four consumer video datasets for studying this task, including evaluations on out-of-image keypoints; and (iii) we show that our method substantially improves PCK and human-subject judgments compared to baselines, both on test videos from the dataset it was trained on, as well as on three other datasets without further adaptation."
Reinforced Axial Refinement Network for Monocular 3D Object Detection
Authors: Lijie Liu; Chufan Wu; Jiwen Lu; Lingxi Xie; Jie Zhou; Qi Tian
Venue: ECCV 2020 (main)

Abstract: Monocular 3D object detection aims to extract the 3D position and properties of objects from a 2D input image. This is an ill-posed problem with a major difficulty lying in the information loss by depth-agnostic cameras. Conventional approaches sample 3D bounding boxes from the space and infer the relationship between the target object and each of them, however, the probability of effective samples is relatively small in the 3D space. To improve the efficiency of sampling, we propose to start with an initial prediction and refine it gradually towards the ground truth, with only one 3d parameter changed in each step. This requires designing a policy which gets a reward after several steps, and thus we adopt reinforcement learning to optimize it. The proposed framework, Reinforced Axial Refinement Network (RAR-Net), serves as a post-processing stage which can be freely integrated into existing monocular 3D detection methods, and improve the performance on the KITTI dataset with small extra computational costs."
Self-Supervised Multi-Task Procedure Learning from Instructional Videos
Authors: Ehsan Elhamifar; Dat Huynh
Venue: ECCV 2020 (main)

Abstract: We address the problem of unsupervised procedure learning from instructional videos of multiple tasks using Deep Neural Networks (DNNs). Unlike existing works, we assume that training videos come from multiple tasks without key-step annotations or grammars, and the goals are to classify a test video to the underlying task and to localize its key-steps. Our DNN learns task-dependent attention features from informative regions of each frame without ground-truth bounding boxes and learns to discover and localize key-steps without key-step annotations by using an unsupervised subset selection module as a teacher. It also learns to classify an input video using the discovered key-steps using a learnable key-step feature pooling mechanism that extracts and learns to combine key-step based features for task recognition. By experiments on two instructional video datasets, we show the effectiveness of our method for unsupervised localization of procedure steps and video classification."
CosyPose: Consistent multi-view multi-object 6D pose estimation
Authors: Yann Labbé; Justin Carpentier; Mathieu Aubry; Josef Sivic
Venue: ECCV 2020 (main)

Abstract: We introduce an approach for recovering the 6D pose of multiple known objects in a scene captured by a set of input images with unknown camera viewpoints. First, we present a single-view single-object 6D pose estimation method, which we use to generate 6D object pose hypotheses. Second, we develop a robust method for matching individual 6D object pose hypotheses across different input images in order to jointly estimate camera viewpoints and 6D poses of all objects in a single consistent scene. Our approach explicitly handles object symmetries, does not require depth measurements, is robust to missing or incorrect object hypotheses, and automatically recovers the number of objects in the scene. Third, we develop a method for global scene refinement given multiple object hypotheses and their correspondences across views. This is achieved by solving an object-level bundle adjustment problem that refines the poses of cameras and objects to minimize the reprojection error in all views. We demonstrate that the proposed method, dubbed Cosy-Pose, outperforms current state-of-the-art results for single-view and multi-view 6D object pose estimation by a large margin on two challenging benchmarks: the YCB-Video and T-LESS datasets. Code and pre-trained models are available on the project webpage."
In-Domain GAN Inversion for Real Image Editing
Authors: Jiapeng Zhu; Yujun Shen; Deli Zhao; Bolei Zhou
Venue: ECCV 2020 (main)

Abstract: Recent work has shown that a variety of semantics emerge in the latent space of Generative Adversarial Networks (GANs) when being trained to synthesize images. However, it is difficult to use these learned semantics for real image editing. A common practice of feeding a real image to a trained GAN generator is to invert it back to a latent code. However, existing inversion methods typically focus on reconstructing the target image by pixel values yet fail to land the inverted code in the semantic domain of the original latent space. As a result, the reconstructed image cannot well support semantic editing through varying the inverted code. To solve this problem, we propose an in-domain GAN inversion approach, which not only faithfully reconstructs the input image but also ensures the inverted code to be semantically meaningful for editing. We first learn a novel domain-guided encoder to project a given image to the native latent space of GANs. We then propose domain-regularized optimization by involving the encoder as a regularizer to fine-tune the code produced by the encoder and better recover the target image. Extensive experiments suggest that our inversion method achieves satisfying real image reconstruction and more importantly facilitates various image editing tasks, significantly outperforming start-of-the-arts."
Key Frame Proposal Network for Efficient Pose Estimation in Videos
Authors: Yuexi Zhang; Yin Wang; Octavia Camps; Mario Sznaier
Venue: ECCV 2020 (main)

Abstract: Human pose estimation in video relies on local information by either estimating each frame independently or tracking poses across frames. In this paper, we propose a novel method combining local approaches with global context. We introduce a light weighted, unsupervised, key-frame proposal network (K-FPN) to select informative frames and a learned dictionary to recover the entire pose sequence from these frames. The K-FPN speeds up the pose estimation and provides robustness to bad frames with occlusion, motion blur, and illumination changes, while the learned dictionary provides global dynamic context. Experiments on Penn Action and sub-JHMDB datasets show that the proposed method achieves state-of-the-art accuracy, with substantial speed-up."
Exchangeable Deep Neural Networks for Set-to-Set Matching and Learning
Authors: Yuki Saito; Takuma Nakamura; Hirotaka Hachiya; Kenji Fukumizu
Venue: ECCV 2020 (main)

Abstract: Matching two different sets of items, called heterogeneous set-to-set matching problem, has recently received attention as a promising problem. The difficulties are to extract features to match a correct pair of different sets and also preserve two types of exchangeability required for set-to-set matching: the pair of sets, as well as the items in each set, should be exchangeable. In this study, we propose a novel deep learning architecture to address the abovementioned difficulties and also an efficient training framework for set-to-set matching. We evaluate the methods through experiments based on two industrial applications: fashion set recommendation and group re-identification. In these experiments, we show that the proposed method provides significant improvements and results compared with the state-of-the-art methods, thereby validating our architecture for the heterogeneous set matching problem."
Making Sense of CNNs: Interpreting Deep Representations & Their Invariances with INNs
Authors: Robin Rombach; Patrick Esser; Björn Ommer
Venue: ECCV 2020 (main)

Abstract: To tackle increasingly complex tasks, it has become an essential ability of neural networks to learn abstract representations. These task-specific representations and, particularly, the invariances they capture turn neural networks into black box models that lack interpretability. To open such a black box, it is, therefore, crucial to uncover the different semantic concepts a model has learned as well as those that it has learned to be invariant to. We present an approach based on INNs that (i) recovers the task-specific, learned invariances by disentangling the remaining factor of variation in the data and that (ii) invertibly transforms these recovered invariances combined with the model representation into an equally expressive one with accessible semantic concepts. As a consequence, neural network representations become understandable by providing the means to (i) expose their semantic meaning, (ii) semantically modify a representation, and (iii) visualize individual learned semantic concepts and invariances. Our invertible approach significantly extends the abilities to understand black box models by enabling post-hoc interpretations of state-of-the-art networks without compromising their performance."
Cross-Modal Weighting Network for RGB-D Salient Object Detection
Authors: Gongyang Li; Zhi Liu; Linwei Ye; Yang Wang; Haibin Ling
Venue: ECCV 2020 (main)

Abstract: Depth maps contain geometric clues for assisting Salient Object Detection (SOD). In this paper, we propose a novel Cross-Modal Weighting (CMW) strategy to encourage comprehensive interactions between RGB and depth channels for RGB-D SOD. Specifically, three RGB-depth interaction modules, named CMW-L, CMW-M and CMW-H, are developed to deal with respectively low-, middle- and high-level cross-modal information fusion. These modules use Depth-to-RGB Weighing (DW) and RGB-to-RGB Weighting (RW) to allow rich cross-modal and cross-scale interactions among feature layers generated by different network blocks. To effectively train the proposed Cross-Modal Weighting Network (CMWNet), we design a composite loss function that summarizes the errors between intermediate predictions and ground truth over different scales. With all these novel components working together, CMWNet effectively fuses information from RGB and depth channels, and meanwhile explores object localization and details across scales. Thorough evaluations demonstrate CMWNet consistently outperforms 15 state-of-the-art RGB-D SOD methods on seven popular benchmarks."
Open-set Adversarial Defense
Authors: Rui Shao; Pramuditha Perera; Pong C. Yuen; Vishal M. Patel
Venue: ECCV 2020 (main)

Abstract: Open-set recognition and adversarial defense study two key aspects of deep learning that are vital for real-world deployment. The objective of open-set recognition is to identify samples from open-set classes during testing, while adversarial defense aims to defend the network against images with imperceptible adversarial perturbations. In this paper, we show that open-set recognition systems are vulnerable to adversarial attacks. Furthermore, we show that adversarial defense mechanisms trained on known classes do not generalize well to open-set samples. Motivated by this observation, we emphasize the need of an Open-Set Adversarial Defense (OSAD) mechanism. This paper proposes an Open-Set Defense Network (OSDN) as a solution to the OSAD problem. The proposed network uses an encoder with feature-denoising layers coupled with a classifier to learn a noise-free latent feature representation. Two techniques are employed to obtain an informative latent feature space with the objective of improving open-set performance. First, a decoder is used to ensure that clean images can be reconstructed from the obtained latent features. Then, self-supervision is used to ensure that the latent features are informative enough to carry out an auxiliary task. We introduce a testing protocol to evaluate OSAD performance and show the effectiveness of the proposed method in multiple object classification datasets. The implementation code of the proposed method is available at: \href{https://github.com/rshaojimmy/ECCV2020-OSAD}{https://github.com/rshaojimmy/ECCV2020-OSAD}."
Deep Image Compression using Decoder Side Information
Authors: Sharon Ayzik; Shai Avidan
Venue: ECCV 2020 (main)

Abstract: We present a Deep Image Compression neural network that relies on side information, which is only available to the decoder. We base our algorithm on the assumption that the image available to the encoder and the image available to the decoder are correlated, and we let the network learn these correlations in the training phase.Then, at run time, the encoder side encodes the input image without knowing anything about the decoder side image and sends it to the decoder. The decoder then uses the encoded input image and the side information image to reconstruct the original image.This problem is known as Distributed Source Coding (DSC) in Information Theory, and we discuss several use cases for this technology. We compare our algorithm to several image compression algorithms and show that adding decoder-only side information does indeed improve results."
Meta-Sim2: Unsupervised Learning of Scene Structure for Synthetic Data Generation
Authors: Jeevan Devaranjan; Amlan Kar; Sanja Fidler
Venue: ECCV 2020 (main)

Abstract: Generation of synthetic data has allowed Machine Learning practitioners to bypass the need for costly collection and labeling of large datasets. Unfortunately the generation of such data often requires experts to carefully design sampling procedures that guarantee creation of realistic scenes. These sampling procedures typically require experts to specify certain structural aspects such as scene layout information. This is often hard to design, as scenes are generally highly complex and diverse. In this paper, we propose a generative model of synthetic scenes that reduces the distribution gap between the scene structure of generated scenes and a real target image dataset. Importantly, since labeling scene structures for real images is incredibly cumbersome, our method operates without any ground truth structure information for real data. Experiments on two synthetic datasets and a real driving dataset show that the method successfully bridges the distribution gap of discrete structural features between real and generated images."
A Generic Visualization Approach for Convolutional Neural Networks
Authors: Ahmed Taha; Xitong Yang; Abhinav Shrivastava; Larry Davis
Venue: ECCV 2020 (main)

Abstract: Retrieval networks are essential for searching and indexing. Compared to classification networks, attention visualization for retrieval networks is hardly studied. We formulate attention visualization as a constrained optimization problem. We leverage the unit L2-Norm constraint as an attention filter (L2-CAF) to localize attention in both classification and retrieval networks. Unlike recent literature, our approach requires neither architectural changes nor fine-tuning. Thus, a pre-trained network's performance is never undermined L2-CAF is quantitatively evaluated using weakly supervised object localization. State-of-the-art results are achieved on classification networks. For retrieval networks, significant improvement margins are achieved over a Grad-CAM baseline. Qualitative evaluation demonstrates how the L2-CAF visualizes attention per frame for a recurrent retrieval network. Further ablation studies highlight the computational cost of our approach and compare L2-CAF with other feasible alternatives. Code available at https://bit.ly/3iDBLFv"
Interactive Annotation of 3D Object Geometry using 2D Scribbles
Authors: Tianchang Shen; Jun Gao; Amlan Kar; Sanja Fidler
Venue: ECCV 2020 (main)

Abstract: Inferring detailed 3D geometry of the scene is crucial for robotics applications, simulation, and 3D content creation. However, such information is hard to obtain, and thus very few datasets support it. In this paper, we propose an interactive framework for annotating 3D object geometry from both point cloud data and RGB imagery. The key idea behind our approach is to exploit strong priors that humans have about the 3D world in order to interactively annotate complete 3D shapes. Our framework targets a wide pool of annotators, i.e. naive users without artistic or graphics expertise. In particular, we introduce two simple-to-use interaction modules. First, we make an automatic guess of the 3D shape and allow the user to provide feedback about large errors by drawing scribbles in desired 2D views. Next, we aim to correct minor errors, in which users drag and drop 3D mesh vertices, assisted by a neural interactive module implemented as a Graph Convolutional Network. Experimentally, we show that only a few user interactions are needed to produce good quality 3D shapes on popular benchmarks such as ShapeNet, Pix3D, and ScanNet. We implement our framework as a web service and conduct a user study, where we show that user annotated data using our method effectively facilitates real-world learning tasks. Our web service will be released. "
Hierarchical Kinematic Human Mesh Recovery
Authors: Georgios Georgakis; Ren Li; Srikrishna Karanam; Terrence Chen; Jana Košecká; Ziyan Wu
Venue: ECCV 2020 (main)

Abstract: We consider the problem of estimating a parametric model of 3D human mesh from a single image. While there has been substantial recent progress in this area with direct regression of model parameters, these methods only implicitly exploit the human body kinematic structure, leading to sub-optimal use of the model prior. In this work, we address this gap by proposing a new technique for regression of human parametric model that is explicitly informed by the known hierarchical structure, including joint interdependencies of the model. This results in a strong prior-informed design of the regressor architecture and an associated hierarchical optimization that is flexible to be used in conjunction with the current standard frameworks for 3D human mesh recovery. We demonstrate these aspects by means of extensive experiments on standard benchmark datasets, showing how our proposed new design outperforms several existing and popular methods, establishing new state-of-the-art results. By considering joint interdependencies, our method is equipped to infer joints even under data corruptions, which we demonstrate by conducting experiments under varying degrees of occlusion."
Multi-Loss Rebalancing Algorithm for Monocular Depth Estimation
Authors: Jae-Han Lee; Chang-Su Kim
Venue: ECCV 2020 (main)

Abstract: An algorithm to combine multiple loss terms adaptively for training a monocular depth estimator is proposed in this work. We construct a loss function space containing tens of losses. Using more losses can improve inference capability without any additional complexity in the test phase. However, when many losses are used, some of them may be neglected during training. Also, since each loss decreases at a different speed, adaptive weighting is required to balance the contributions of the losses. To address these issues, we propose the loss rebalancing algorithm that initializes and rebalances the weight for each loss function adaptively in the course of training. Experimental results show that the proposed algorithm provides state-of-the-art depth estimation results on various datasets. Codes are available at https://github.com/jaehanlee-mcl/multi-loss-rebalancing-depth."