pwc-archive/papers-with-abstracts huggingface dataset:
SSN: Shape Signature Networks for Multi-class Object Detection from Point Clouds
Authors: Xinge Zhu      Yuexin Ma      Tai Wang      Yan Xu Jianping Shi      Dahua Lin
Date: 2020-08-01 00:00:00
Proceeding: ECCV 2020 8
Tasks: 3D Object Detection, object-detection, Object Detection

Multi-class 3D object detection aims to classify and localize objects of multiple categories from point clouds. Due to the nature of point clouds, i.e. unstructured, sparse and noisy, some features benefit-ting multi-class discrimination are underexploited, such as shape information. In this paper, we propose a novel 3D shape signature to explore the shape information from point clouds. By incorporating operations of symmetry, convex hull and chebyshev fitting, the proposed shape sig-nature is not only compact and effective but also robust to the noise, which serves as a soft constraint to improve the feature capability of multi-class discrimination. Based on the proposed shape signature, we develop the shape signature networks (SSN) for 3D object detection, which consist of pyramid feature encoding part, shape-aware grouping heads and explicit shape encoding objective. Experiments show that the proposed method performs remarkably better than existing methods on two large-scale datasets. Furthermore, our shape signature could act as a plug-and-play component and ablation study shows its effectiveness and good scalability
OID: Outlier Identifying and Discarding in Blind Image Deblurring
Authors: Liang Chen, Faming Fang, Jiawei Zhang, Jun Liu, Guixu Zhang
Date: 2020-08-01 00:00:00
Proceeding: ECCV 2020 8
Tasks: Blind Image Deblurring, Deblurring, Image Deblurring

Blind deblurring methods are sensitive to outliers, such as saturated pixels and non-Gaussian noise. Even a small amount of outliers can dramatically degrade the quality of the estimated blur kernel, because the outliers are not conforming to the linear formation of the blurring process. Prior arts develop sophisticated edge-selecting steps or noise filtering pre-processing steps to deal with outliers (i.e. indirect approaches). However, these indirect approaches may fail when massive outliers are presented, since informative details may be polluted by outliers or erased during the pre-processing steps. To address these problems, this paper develops a simple yet effective Outlier Identifying and Discarding (OID) method, which alleviates limitations in existing Maximum A Posteriori (MAP)- based deblurring models when significant outliers are presented. Unlike previous indirect outlier processing methods, OID tackles outliers directly by explicitly identifying and discarding them, when updating both the latent image and the blur kernel during the deblurring process, where the outliers are detected by using the sparse and entropy-based modules. OID is easy to implement and extendable for non-blind restoration. Extensive experiments demonstrate the superiority of OID against recent works both quantitatively and qualitatively.
Enhanced Sparse Model for Blind Deblurring
Authors: Liang Chen, Faming Fang, Shen Lei, Fang Li, Guixu Zhang
Date: 2020-08-01 00:00:00
Proceeding: ECCV 2020 8
Tasks: Deblurring, model

Existing arts have shown promising efforts to deal with the blind deblurring task. However, most of the recent works assume the additive noise involved in the blurring process to be simple-distributed (i.e. Gaussian or Laplacian), while the real-world case is proved to be much more complicated. In this paper, we develop a new term to better fit the complex natural noise. Specifically, we use a weighted combination of a dense function (i.e. l2) and a newly designed enhanced sparse model termed as le, which is developed from two sparse models (i.e. l1 and l0), to fulfill the task. Moreover, we further suggest using le to regularize image gradients. Compared to the widely-adopted l0 sparse term, le can penalize more insignificant image details (Fig. 1). Based on the half-quadratic splitting method, we provide an effective scheme to optimize the overall formulation. Comprehensive evaluations on public datasets and real-world images demonstrate the superiority of the proposed method against state-of-the-art methods in terms of both speed and accuracy.
Feature Normalized Knowledge Distillation for Image Classification
Authors: Kunran Xu, Lai Rui, Yishi Li, Lin Gu
Date: 2020-08-01 00:00:00
Proceeding: ECCV 2020 8
Tasks: Classification, General Classification, image-classification, Image Classification, Knowledge Distillation

Knowledge Distillation (KD) transfers the knowledge from a cumbersome teacher model to a lightweight student network. Since a single image may reasonably relate to several categories, the one-hot label would inevitably introduce the encoding noise. From this perspective, we systematically analyze the distillation mechanism and demonstrate that the L2-norm of the feature in penultimate layer would be too large under the influence of label noise, and the temperature T in KD could be regarded as a correction factor for L2-norm to suppress the impact of noise. Noticing different samples suffer from varying intensities of label noise, we further propose a simple yet effective feature normalized knowledge distillation which introduces the sample specific correction factor to replace the unified temperature T for better reducing the impact of noise. Extensive experiments show that the proposed method surpasses standard KD as well as self-distillation significantly on Cifar-100, CUB-200-2011 and Stanford Cars datasets. The codes are in https://github.com/aztc/FNKD
FTL: A universal framework for training low-bit DNNs via Feature Transfer
Authors: Kunyuan Du, Ya zhang, Haibing Guan, Qi Tian, Shenggan Cheng, James Lin
Date: 2020-08-01 00:00:00
Proceeding: ECCV 2020 8
Tasks: Quantization, Transfer Learning

Low-bit Deep Neural Networks (low-bit DNNs) have recently received significant attention for their high efficiency. However, low-bit DNNs are often difficult to optimize due to the the saddle points in loss surfaces. Here we introduce a novel feature-based knowledge transfer framework, which utilizes a 32-bit DNN to guide the training of a low-bit DNN via feature maps. It is challenge because feature maps from two branches lie in continuous and discrete space respectively, and such mismatch has not been handled properly by existing feature transfer frameworks. In this paper, we propose to directly transfer information-rich continuous-space feature to the low-bit branch. To alleviate the negative impacts brought by the feature quantizer during the transfer process, we make two branches interact via centered cosine distance rather than the widely-used p-norms. Extensive experiments are conducted on Cifar10/100 and ImageNet. Compared with low-bit models trained directly, the proposed framework brings 0.5% to 3.4% accuracy gains to three different quantization schemes. Besides, the proposed framework can also be combined with other techniques, e.g. logits transfer, for further enhacement.
PL₁P - Point-line Minimal Problems under Partial Visibility in Three Views
Authors: Timothy Duff, Kathlén Kohn, Anton Leykin, Tomas Pajdla
Date: 2020-08-01 00:00:00
Proceeding: ECCV 2020 8
Tasks: 3D Reconstruction

We present a complete classification of minimal problems for generic arrangements of points and lines in space observed partially by three calibrated perspective cameras when each line is incident to at most one point. This is a large class of interesting minimal problems that allows for missing observations in images due to occlusions and missed detections. There is an infinite number of such minimal problems; however, we show that they can be reduced to 140616 equivalence classes by removing superfluous features and relabeling the cameras. We also introduce camera-minimal problems, which are practical for designing minimal solvers, and show how to pick a simplest camera-minimal problem for each minimal problem. This simplification results in 74575 equivalence classes. Only 76 of these were known; the rest are new. In order to identify problems that have potential for practical solving of image matching and 3D reconstruction, we present several smaller natural subfamilies of camera-minimal problems as well as compute solution counts for all camera-minimal problems which have less than 300 solutions for generic data.
Prediction and Recovery for Adaptive Low-Resolution Person Re-Identification
Authors: Ke Han, Yan Huang, Zerui Chen, Liang Wang, Tieniu Tan
Date: 2020-08-01 00:00:00
Proceeding: ECCV 2020 8
Tasks: Person Re-Identification, Super-Resolution

Low-resolution person re-identification (LR re-id) is a challenging task with low-resolution probes and high-resolution gallery images. To address the resolution mismatch, existing methods typically recover missing details for low-resolution probes by super-resolution. However, they usually pre-specify fixed scale factors for all images, and ignore the fact that choosing a preferable scale factor for certain image content probably greatly benefits the identification. In this paper, we propose a novel Prediction, Recovery and Identification (PRI) model for LR re-id, which adaptively recovers missing details by predicting a preferable scale factor based on the image content. To deal with the lack of ground-truth optimal scale factors, our model contains a self-supervised scale factor metric that automatically generates dynamic soft labels. The generated labels indicate probabilities that each scale factor is optimal, which are used as guidance to enhance the content-aware scale factor prediction. Consequently, our model can more accurately predict and recover the content-aware details, and achieve state-of-the-art performances on four LR re-id datasets.
RBF-Softmax: Learning Deep Representative Prototypes with Radial Basis Function Softmax
Authors: Xiao Zhang, Rui Zhao, Yu Qiao, Hongsheng Li
Date: 2020-08-01 00:00:00
Proceeding: ECCV 2020 8

Deep neural networks have achieved remarkable successes in learning feature representations for visual classification. However, deep features learned by the softmax cross-entropy loss generally show excessive intra-class variations. We argue that, because the traditional softmax losses aim to optimize only the relative differences between intra-class and inter-class distances (logits), it cannot obtain representative class prototypes (class weights/centers) to regularize intra-class distances, even when the training is converged. Previous efforts mitigate this problem by introducing auxiliary regularization losses. But these modified losses mainly focus on optimizing intra-class compactness, while ignoring keeping reasonable relations between different class prototypes. These lead to weak models and eventually limit their performance. To address this problem, this paper introduces a novel Radial Basis Function (RBF) distances to replace the commonly used inner products in the softmax loss function, such that it can adaptively assign losses to regularize the intra-class and inter-class distances by reshaping the relative differences, and thus creating more representative prototypes of classes to improve optimization. The proposed RBF-Softmax loss function not only effectively reduces intra-class distances, stabilizes the training behavior, and reserves ideal relations between prototypes, but also significantly improves the testing performance. Experiments on visual recognition benchmarks including MNIST, CIFAR-10/100, and ImageNet demonstrate that the proposed RBF-Softmax achieves better results than cross-entropy and other state-of-the-art classification losses. The code is at https://github.com/2han9x1a0release/RBF-Softmax.
Determining the Relevance of Features for Deep Neural Networks
Authors: Christian Reimers, Jakob Runge, Joachim Denzler
Date: 2020-08-01 00:00:00
Proceeding: ECCV 2020 8
Tasks: Causal Inference

Deep neural networks are tremendously successful in many applications, but end-to-end trained networks often result in hard to understand black-box classifiers or predictors. In this work, we present a novel method to identify whether a specific feature is relevant to a classifier’s decision or not. This relevance is determined at the level of the learned mapping, instead of for a single example. The approach does neither need retraining of the network nor information on intermediate results or gradients. The key idea of our approach builds upon concepts from causal inference. We interpret machine learning in a structural causal model and use Reichenbach’s common cause principle to infer whether a feature is relevant. We demonstrate empirically that the method is able to successfully evaluate the relevance of given features on three real-life data sets, namely MS COCO, CUB200 and HAM10000.
Weakly Supervised Semantic Segmentation with Boundary Exploration
Authors: Liyi Chen, Weiwei Wu, Chenchen Fu, Xiao Han, Yuntao Zhang
Date: 2020-08-01 00:00:00
Proceeding: ECCV 2020 8
Tasks: Segmentation, Semantic Segmentation, Weakly supervised Semantic Segmentation, Weakly-Supervised Semantic Segmentation

Weakly supervised semantic segmentation with image-level labels has attracted a lot of attention recently because these labels are already available in most datasets. To obtain semantic segmentation under weak supervision, this paper presents a simple yet effective approach based on the idea of explicitly exploring object boundaries from training images to keep coincidence of segmentation and boundaries. Specifically, we synthesize boundary annotations by exploiting coarse localization maps obtained from CNN classifier, and use annotations to train the proposed network called BENet which further excavates more object boundaries to provide constraints for segmentation. Finally generated pseudo annotations of training images are used to supervise an off-the-shelf segmentation network. We evaluate the proposed method on PASCAL VOC 2012 benchmark and the final results achieve 65.7% and 66.6% mIoU scores on val and test sets respectively, which outperforms previous methods trained under image-level supervision.
Weight Decay Scheduling and Knowledge Distillation for Active Learning
Authors: Juseung Yun, Byungjoo Kim, Junmo Kim
Date: 2020-08-01 00:00:00
Proceeding: ECCV 2020 8
Tasks: Active Learning, Knowledge Distillation, Scheduling

Although convolutional neural networks perform extremely well for numerous computer vision tasks, a considerably large amount of labeled data is required to ensure a good outcome. Data labeling is labor-intensive, and in some cases, the labeling budget may be limited. Active learning is a technique that can reduce the labeling required. With this technique, the neural network selects on its own the unlabeled data most helpful for learning, and then requests the human annotator for the labels. Most existing active learning methods have focused on acquisition functions for an effective selection of the informative samples. However, in this paper, we focus on the data-incremental nature of active learning, and propose a method for properly tuning the weight decay as the amount of data increases. We also demonstrate that the performance can be improved by knowledge distillation using a low-performance teacher model trained from the previous acquisition step. In addition, we present a novel perspective of the weight decay, which provides a regularization effect by limiting the number of effective parameters and channels in the convolutional filter. We validate our methods on the MNIST, CIFAR-10, and CIFAR-100 datasets using convolutional neural networks of various sizes.
More Classifiers, Less Forgetting: A Generic Multi-classifier Paradigm for Incremental Learning
Authors: Yu Liu, Sarah Parisot, Gregory Slabaugh, Xu Jia, Ales Leonardis, Tinne Tuytelaars
Date: 2020-08-01 00:00:00
Proceeding: ECCV 2020 8
Tasks: Incremental Learning

Less Forgetting: A Generic Multi-classifier Paradigm for Incremental Learning","Overcoming catastrophic forgetting in neural networks is a long-standing and core research objective for incremental learning. Notable studies have shown regularization strategies enable the network to remember previously acquired knowledge devoid of heavy forgetting. Since those regularization strategies are mostly associated with classifier outputs, we propose a MUlti-Classifier (MUC) incremental learning paradigm that integrates an ensemble of auxiliary classifiers to estimate more effective regularization constraints. Additionally, we extend two common methods, focusing on parameter and activation regularization, from the conventional single-classifier paradigm to MUC. Our classifier ensemble promotes regularizing network parameters or activations when moving to learn the next task. Under the setting of task-agnostic evaluation, our experimental results on CIFAR-100 and Tiny ImageNet incremental benchmarks show that our method outperforms other baselines. Specifically, MUC obtains 3%-5% accuracy boost and 4%-5% decline of forgetting ratio, compared with MAS and LwF. Our code is available at https://github.com/Liuy8/MUC.
Multi-Source Open-Set Deep Adversarial Domain Adaptation
Authors: Sayan Rakshit, Dipesh Tamboli, Pragati Shuddhodhan Meshram, Biplab Banerjee, Gemma Roig, Subhasis Chaudhuri
Date: 2020-08-01 00:00:00
Proceeding: ECCV 2020 8
Tasks: Domain Adaptation, Unsupervised Domain Adaptation

We introduce a novel learning paradigm based on multi-source open-set unsupervised domain adaptation (MS-OSDA). Recently, the notion of single-source open-set domain adaptation (OSDA) has drawn much attention which considers the presence of previously unseen open-set (unknown) classes in the target-domain in addition to the source-domain closed-set (known) classes. It is reasonable to assume that labeled samples may be distributed over multiple source-domains while the target-domain is equipped with both the closed-set and open-set data. However, the existing single-source OSDA techniques cannot be directly extended to such a multi-source scenario considering the inhomogeneities present among the different source-domains. As a remedy, we propose a novel adversarial learning-driven approach to deal with the MS-OSDA setup. Precisely, we model a shared feature space for all the domains while encouraging fine-grained alignment among the known-class samples. Besides, an adversarial learning strategy is followed to model the discriminator between the target-domain known and unknown classes. We validate our method on the Office-31, Office-Home, Office-CalTech, and Digits datasets and find our model to consistently outperform the relevant literature.
Neural Batch Sampling with Reinforcement Learning for Semi-Supervised Anomaly Detection
Authors: Wen-Hsuan Chu, Kris M. Kitani
Date: 2020-08-01 00:00:00
Proceeding: ECCV 2020 8
Tasks: Anomaly Detection, reinforcement-learning, Reinforcement Learning (RL), Semi-supervised Anomaly Detection, Supervised Anomaly Detection

We are interested in the detection and segmentation of anomalies in images where the anomalies are typically small (i.e., a small tear in woven fabric, broken pin of an IC chip). From a statistical learning point of view, anomalies have low occurrence probability and are not from the main modes of a data distribution. Learning a generative model of anomalous data from a natural distribution of data can be difficult because the data distribution is heavily skewed towards a large amount of non-anomalous data. When training a generative model on such imbalanced data using an iterative learning algorithm like stochastic gradient descent (SGD), we observe an expected yet interesting trend in the loss values (a measure of the learned models performance) after each gradient update across data samples. Naturally, as the model sees more non-anomalous data during training, the loss values over a non-anomalous data sample decreases, while the loss values on an anomalous data sample fluctuates. In this work, our key hypothesis is that this change in loss values during training can be used as a feature to identify anomalous data. In particular, we propose a novel semi-supervised learning algorithm for anomaly detection and segmentation using an anomaly classifier that uses as input the extit{loss profile} of a data sample processed through an autoencoder. The loss profile is defined as a sequence of reconstruction loss values produced during iterative training. To amplify the difference in loss profiles between anomalous and non-anomalous data, we also introduce a Reinforcement Learning based meta-algorithm, which we call the neural batch sampler, to strategically sample training batches during autoencoder training. Experimental results on multiple datasets with a high diversity of textures and objects, often with multiple modes of defects within them, demonstrate the capabilities and effectiveness of our method when compared with existing state-of-the-art baselines.
Proposal-based Video Completion
Authors: Yuan-Ting Hu, Heng Wang, Nicolas Ballas, Kristen Grauman, Alexander G. Schwing
Date: 2020-08-01 00:00:00
Proceeding: ECCV 2020 8
Tasks: Image Inpainting, object-detection, Object Detection, One-shot visual object segmentation, Video Inpainting, Video Restoration

Video inpainting is an important technique for a wide variety of applications from video content editing to video restoration. Early approaches follow image inpainting paradigms, but are challenged by complex camera motion and non-rigid deformations. To address these challenges flow-guided propagation techniques have been proposed. However, computation of flow is non-trivial for unobserved regions and propagation across a whole video sequence is computationally demanding. In contrast, in this paper, we propose a video inpainting algorithm based on proposals: we use 3D convolutions to obtain an initial inpainting estimate which is subsequently refined by fusing a generated set of proposals. Different from existing approaches for video inpainting, and inspired by well-explored mechanisms for object detection, we argue that proposals provide a rich source of information that permits to combine similarly looking patches that may be spatially and temporally far from the region to be inpainted. We validate the effectiveness of our method on the challenging YouTube VOS and DAVIS datasets using different settings and demonstrate results outperforming state-of-the-art on standard metrics.
HGNet: Hybrid Generative Network for Zero-shot Domain Adaptation
Authors: Haifeng Xia, Zhengming Ding
Date: 2020-08-01 00:00:00
Proceeding: ECCV 2020 8
Tasks: Domain Adaptation

Domain Adaptation as an important tool aims to explore a generalized model trained on well-annotated source knowledge to address learning issue on target domain with insufficient or even no annotation. Current approaches typically incorporate data from source and target domains for training stage to deal with domain shift. However, most domain adaptation tasks generally suffer from the problem that measuring the domain shift tends to be impossible when target data is inaccessible. In this paper, we propose a novel algorithm, Hybrid Generative Network (HGNet) for Zero-shot Domain Adaptation, which embeds an adaptive feature separation (AFS) module into generative architecture. Specifically, AFS module can adaptively distinguish classification-relevant features from classification-irrelevant ones to learn domain-invariant and discriminative representations when task-relevant target instances are invisible. To learn high-quality feature representation, we also develop hybrid generative strategy to ensure the uniqueness of feature separation and completeness of semantic information. Extensive experimental results on several benchmarks illustrate that our method achieves more promising results than state-of-the-art approaches.