GenAI4ELab/papercli-papers huggingface dataset:
Class-Incremental Novel Class Discovery
Authors: Subhankar Roy; Mingxuan Liu; Zhun Zhong; Nicu Sebe; Elisa Ricci
Venue: ECCV 2022 (main)

Abstract: "We study the new task of class-incremental Novel Class Discovery (class-iNCD), which refers to the problem of discovering novel categories in an unlabelled data set by leveraging a pre-trained model that has been trained on a labelled data set containing disjoint yet related categories. Apart from discovering novel classes, we also aim at preserving the ability of the model to recognize previously seen base categories. Inspired by rehearsal-based incremental learning methods, in this paper we propose a novel approach for class-iNCD which prevents forgetting of past information about the base classes by jointly exploiting base class feature prototypes and feature-level knowledge distillation. We also propose a self-training clustering strategy that simultaneously clusters novel categories and trains a joint classifier for both the base and novel classes. This makes our method able to operate in a class-incremental setting. Our experiments, conducted on three common benchmarks, demonstrate that our method significantly outperforms state-of-the-art approaches. Code is available at \url{https://github.com/OatmealLiu/class-iNCD}."
Unknown-Oriented Learning for Open Set Domain Adaptation
Authors: Jie Liu; Xiaoqing Guo; Yixuan Yuan
Venue: ECCV 2022 (main)

Abstract: "Open set domain adaptation (OSDA) aims to tackle the distribution shift of partially shared categories between the source and target domains, meanwhile identifying target samples non-appeared in source domain. The key issue behind this problem is to classify these various unseen samples as unknown category with the absent of relevant knowledge from the source domain. Though impressing performance, existing works neglect the complex semantic information and huge intra-category variation of unknown category, incapable of representing the complicated distribution. To overcome this, we propose a novel Unknown-Oriented Learning (UOL) framework for OSDA, and it is composed of three stages: true unknown excavation, false unknown suppression and known alignment. Specifically, to excavate the diverse semantic information in unknown category, the multi-unknown detector (MUD) equipped with weight discrepancy constraint is proposed in true unknown excavation. During false unknown suppression, Source-to-Target grAdient Graph (S2TAG) is constructed to select reliable target samples with the proposed super confidence criteria. Then, Target-to-Target grAdient Graph (T2TAG) exploits the geometric structure in gradient manifold to obtain confident pseudo labels for target data. At the last stage, known alignment, the known samples in the target domain are aligned with the source domain to alleviate the domain gap. Extensive experiments demonstrate the superiority of our method compared with state-of-the-art methods on three benchmarks."
Prototype-Guided Continual Adaptation for Class-Incremental Unsupervised Domain Adaptation
Authors: Hongbin Lin; Yifan Zhang; Zhen Qiu; Shuaicheng Niu; Chuang Gan; Yanxia Liu; Mingkui Tan
Venue: ECCV 2022 (main)

Abstract: "This paper studies a new, practical but challenging problem, called Class-Incremental Unsupervised Domain Adaptation (CI-UDA), where the labeled source domain contains all classes, but the classes in the unlabeled target domain increase sequentially. This problem is challenging due to two difficulties. First, source and target label sets are inconsistent at each time step, which makes it difficult to conduct accurate domain alignment. Second, previous target classes are unavailable in the current step, resulting in the forgetting of previous knowledge. To address this problem, we propose a novel Prototype-guided Continual Adaptation (ProCA) method, consisting of two solution strategies. 1) Label prototype identification: we identify target label prototypes by detecting shared classes with cumulative prediction probabilities of target samples. 2) Prototype-based alignment and replay: based on the identified label prototypes, we align both domains and enforce the model to retain previous knowledge. With these two strategies, ProCA is able to adapt the source model to a class-incremental unlabeled target domain effectively. Extensive experiments demonstrate the effectiveness and superiority of ProCA in resolving CI-UDA. The source code is available at https://github.com/Hongbin98/ProCA.git."
DecoupleNet: Decoupled Network for Domain Adaptive Semantic Segmentation
Authors: Xin Lai; Zhuotao Tian; Xiaogang Xu; Yingcong Chen; Shu Liu; Hengshuang Zhao; Liwei Wang; Jiaya Jia
Venue: ECCV 2022 (main)

Abstract: "Unsupervised domain adaptation in semantic segmentation alleviates the reliance on expensive pixel-wise annotation. It uses a labeled source domain dataset as well as unlabeled target domain images to learn a segmentation network. In this paper, we observe two main issues of existing domain-invariant learning framework. (1) Being distracted by the feature distribution alignment, the network cannot focus on the segmentation task. (2) Fitting source domain data well would compromise the target domain performance. To address these issues, we propose DecoupleNet to alleviate source domain overfitting and let the final model focus more on the segmentation task. Also, we put forward Self-Discrimination (SD) and introduce an auxiliary classifier to learn more discriminative target domain features with pseudo labels. Finally, we propose Online Enhanced Self-Training (OEST) to contextually enhance the quality of pseudo labels in an online manner. Experiments show our method outperforms existing state-of-the-art methods. Extensive ablation studies verify the effectiveness of each component. Code is available at https://github.com/dvlab-research/DecoupleNet."
Class-Agnostic Object Counting Robust to Intraclass Diversity
Authors: Shenjian Gong; Shanshan Zhang; Jian Yang; Dengxin Dai; Bernt Schiele
Venue: ECCV 2022 (main)

Abstract: "Most previous works on object counting are limited to pre-defined categories. In this paper, we focus on classagnostic counting, i.e., counting object instances in an image by simply specifying a few exemplar boxes of interest. We start with an analysis on intraclass diversity and point out three factors: color, shape and scale diversity seriously hurts counting performance. Motivated by this analysis, we propose a new counter robust to high intraclass diversity, for which we propose two effective modules: Exemplar Feature Augmentation (EFA) and Edge Matching (EM). Aiming to handle diversity from all aspects, EFA generates a large variety of exemplars in the feature space based on the provided exemplars. Additionally, the edge matching branch focuses on the more reliable cue of shape, making our counter more robust to color variations. Experimental results on standard benchmarks show that our Robust Class-Agnostic Counter (RCAC) achieves state-of-the-art performance. The code is publicly available at https://github.com/Yankeegsj/RCAC."
Burn after Reading: Online Adaptation for Cross-Domain Streaming Data
Authors: Luyu Yang; Mingfei Gao; Zeyuan Chen; Ran Xu; Abhinav Shrivastava; Chetan Ramaiah
Venue: ECCV 2022 (main)

Abstract: In the context of online privacy, many methods propose complex security preserving measures to protect sensitive data. In this paper, we note that: not storing any sensitive data is the best form of security. We propose an online framework called "Burn After Reading", i.e. each online sample is permanently deleted after it is processed. Our framework utilizes the labels from the public data and predicts on the unlabeled sensitive private data. To tackle the inevitable distribution shift from the public data to the private data, we propose a novel domain adaptation algorithm that directly aims at the fundamental challenge of this online setting--the lack of diverse source-target data pairs. We design a Cross-Domain Bootstrapping approach, named CroDoBo, to increase the combined data diversity across domains. To fully exploit the valuable discrepancies among the diverse combinations, we employ the training strategy of multiple learners with co-supervision. CroDoBo achieves state-of-the-art online performance on four domain adaptation benchmarks. Code is provided.
Mind the Gap in Distilling StyleGANs
Authors: Guodong Xu; Yuenan Hou; Ziwei Liu; Chen Change Loy
Venue: ECCV 2022 (main)

Abstract: "StyleGAN family is one of the most popular Generative adversarial networks (GANs) for unconditional generation. Despite its impressive performance, its high demand on storage and computation impedes their deployment on resource-constrained devices. This paper provides a comprehensive study of distilling from the popular StyleGAN-like architecture. Our key insight is that the main challenge of StyleGAN distillation lies in the output discrepancy issue, where the teacher and student model yield different outputs given the same input latent code. Standard knowledge distillation losses typically fail under this heterogeneous distillation scenario. We conduct thorough analysis about the reasons and effects of this discrepancy issue, and identify that the style module plays a vital role in determining semantic information of generated images. Based on this finding, we propose a novel initialization strategy for the student model, which can ensure the output consistency to the maximum extent. To further enhance the semantic consistency between the teacher and student model, we present a latent-direction-based distillation loss that preserves the semantic relations in latent space. Extensive experiments demonstrate the effectiveness of our approach in distilling StyleGAN2 and StyleGAN3, outperforming existing GAN distillation methods by a large margin. Code and models will be released."
Improving Test-Time Adaptation via Shift-Agnostic Weight Regularization and Nearest Source Prototypes
Authors: Sungha Choi; Seunghan Yang; Seokeon Choi; Sungrack Yun
Venue: ECCV 2022 (main)

Abstract: "This paper proposes a novel test-time adaptation strategy that adjusts the model pre-trained on the source domain using only unlabeled online data from the target domain to alleviate the performance degradation due to the distribution shift between the source and target domains. Adapting the entire model parameters using the unlabeled online data may be detrimental due to the erroneous signals from an unsupervised objective. To mitigate this problem, we propose a shift-agnostic weight regularization that encourages largely updating the model parameters sensitive to distribution shift while slightly updating those insensitive to the shift, during test-time adaptation. This regularization enables the model to quickly adapt to the target domain without performance degradation by utilizing the benefit of a high learning rate. In addition, we present an auxiliary task based on nearest source prototypes to align the source and target features, which helps reduce the distribution shift and leads to further performance improvement. We show that our method exhibits state-of-the-art performance on various standard benchmarks and even outperforms its supervised counterpart."
Learning Instance-Specific Adaptation for Cross-Domain Segmentation
Authors: Yuliang Zou; Zizhao Zhang; Chun-Liang Li; Han Zhang; Tomas Pfister; Jia-Bin Huang
Venue: ECCV 2022 (main)

Abstract: "We propose a test-time adaptation method for cross-domain image segmentation. Our method is simple: Given a new unseen instance at the test time, we adapt a pre-trained model by conducting instance-specific BatchNorm (statistics) calibration. Our approach has two core components. First, we replace the manually designed BatchNorm calibration rule with a learnable module. Second, we leverage strong data augmentation to simulate random domain shifts for learning the calibration rule. In contrast to existing domain adaptation methods, our method does not require accessing the target domain data at training time or conducting computationally expensive test-time model training/optimization. Equipping our method with models trained by standard recipes achieves significant improvement, comparing favorably with several state-of-the-art domain generalization and one-shot unsupervised domain adaptation approaches. Combining our method with the domain generalization methods further improves the performance, reaching a new state of the art."
RegionCL: Exploring Contrastive Region Pairs for Self-Supervised Representation Learning
Authors: Yufei Xu; Qiming Zhang; Jing Zhang; Dacheng Tao
Venue: ECCV 2022 (main)

Abstract: "Self-supervised methods (SSL) have achieved significant success via maximizing the mutual information between two augmented views, where cropping is a popular augmentation technique. Cropped regions are widely used to construct positive pairs, while the remained regions after cropping have rarely been explored in existing methods, although they together constitute the same image instance and both contribute to the description of the category. In this paper, we make the first attempt to demonstrate the importance of both regions in cropping from a complete perspective and the effectiveness of using both regions via designing a simple yet effective pretext task called Region Contrastive Learning (RegionCL). Technically, to construct the two kinds of regions, we randomly crop a region (called the paste view) from each input image with the same size and swap them between different images to compose new images together with the remained regions (called the canvas view). Then, instead of taking the new images as a whole for positive or negative samples, contrastive pairs are efficiently constructed from the regional perceptive based on the following simple criteria, i.e., each view is (1) positive with views augmented from the same original image and (2) negative with views augmented from other images. With minor modifications to popular SSL methods, RegionCL exploits those abundant pairs and helps the model distinguish the regions features from both canvas and paste views, therefore learning better visual representations. Experiments on ImageNet, MS COCO, and Cityscapes demonstrate that RegionCL improves MoCov2, DenseCL, and SimSiam by large margins and achieves state-of-the-art performance on classification, detection, and segmentation tasks."
Long-Tailed Class Incremental Learning
Authors: Xialei Liu; Yu-Song Hu; Xu-Sheng Cao; Andrew D. Bagdanov; Ke Li; Ming-Ming Cheng
Venue: ECCV 2022 (main)

Abstract: "In class incremental learning (CIL) a model must learn new classes in a sequential manner without forgetting old ones. However, conventional CIL methods consider a balanced distribution for each new task, which ignores the prevalence of long-tailed distributions in the real world. In this work we propose two long-tailed CIL scenarios, which we term Ordered and Shuffled LT-CIL. Ordered LT-CIL considers the scenario where we learn from head classes collected with more samples than tail classes which have few. Shuffled LT-CIL, on the other hand, assumes a completely random long-tailed distribution for each task. We systematically evaluate existing methods in both LT-CIL scenarios and demonstrate very different behaviors compared to conventional CIL scenarios. Additionally, we propose a two-stage learning baseline with a learnable weight scaling layer for reducing the bias caused by long-tailed distribution in LT-CIL and which in turn also improves the performance of conventional CIL due to the limited exemplars. Our results demonstrate the superior performance (up to 6.44 points in average incremental accuracy) of our approach on CIFAR-100 and ImageNet-Subset. The code is available at https://github.com/xialeiliu/Long-Tailed-CIL."
DLCFT: Deep Linear Continual Fine-Tuning for General Incremental Learning
Authors: Hyounguk Shon; Janghyeon Lee; Seung Hwan Kim; Junmo Kim
Venue: ECCV 2022 (main)

Abstract: "Pre-trained representation is one of the key elements in the success of modern deep learning. However, existing works on continual learning methods have mostly focused on learning models incrementally from scratch. In this paper, we explore an alternative framework to incremental learning where we continually fine-tune the model from a pre-trained representation. Our method takes advantage of linearization technique of a pre-trained neural network for simple and effective continual learning. We show that this allows us to design a linear model where quadratic parameter regularization method is placed as the optimal continual learning policy, and at the same time enjoying the high performance of neural networks. We also show that the proposed algorithm enables parameter regularization methods to be applied to class-incremental problems. Additionally, we provide a theoretical reason why the existing parameter-space regularization algorithms such as EWC underperform on neural networks trained with cross-entropy loss. We show that the proposed method can prevent forgetting while achieving high continual fine-tuning performance on image classification tasks. To show that our method can be applied to general continual learning settings, we evaluate our method in data-incremental, task-incremental, and class-incremental learning problems."
Adversarial Partial Domain Adaptation by Cycle Inconsistency
Authors: Kun-Yu Lin; Jiaming Zhou; Yukun Qiu; Wei-Shi Zheng
Venue: ECCV 2022 (main)

Abstract: "Unsupervised partial domain adaptation (PDA) is a unsupervised domain adaptation problem which assumes that the source label space subsumes the target label space. A critical challenge of PDA is the negative transfer problem, which is triggered by learning to match the whole source and target domains. To mitigate negative transfer, we note a fact that, it is impossible for a source sample of outlier classes to find a target sample of the same category due to the absence of outlier classes in the target domain, while it is possible for a source sample of shared classes. Inspired by this fact, we exploit the cycle inconsistency, i.e., category discrepancy between the original features and features after cycle transformations, to distinguish outlier classes apart from shared classes in the source domain. Accordingly, we propose to filter out source samples of outlier classes by weight suppression and align the distributions of shared classes between the source and target domains by adversarial learning. To learn accurate weight assignment for filtering out outlier classes, we design cycle transformations based on domain prototypes and soft nearest neighbor, where center losses are introduced in individual domains to reduce the intra-class variation. Experiment results on three benchmark datasets demonstrate the effectiveness of our proposed method."
Combating Label Distribution Shift for Active Domain Adaptation
Authors: Sehyun Hwang; Sohyun Lee; Sungyeon Kim; Jungseul Ok; Suha Kwak
Venue: ECCV 2022 (main)

Abstract: "We consider the problem of active domain adaptation (ADA) to unlabeled target data, of which subset is actively selected and labeled given a budget constraint. Inspired by recent analysis on a critical issue from label distribution mismatch between source and target in domain adaptation, we devise a method that addresses the issue for the first time in ADA. At its heart lies a novel sampling strategy, which seeks target data that best approximate the entire target distribution as well as being representative, diverse, and uncertain. The sampled target data are then used not only for supervised learning but also for matching label distributions of source and target domains, leading to remarkable performance improvement. On four public benchmarks, our method substantially outperforms existing methods in every adaptation scenario."
GIPSO: Geometrically Informed Propagation for Online Adaptation in 3D LiDAR Segmentation
Authors: Cristiano Saltori; Evgeny Krivosheev; Stéphane Lathuilière; Nicu Sebe; Fabio Galasso; Giuseppe Fiameni; Elisa Ricci; Fabio Poiesi
Venue: ECCV 2022 (main)

Abstract: "3D point cloud semantic segmentation is fundamental for autonomous driving. Most approaches in the literature neglect an important aspect, i.e., how to deal with domain shift when handling dynamic scenes. This can significantly hinder the navigation capabilities of self-driving vehicles. This paper advances the state of the art in this research field. Our first contribution consists in analysing a new unexplored scenario in point cloud segmentation, namely Source-Free Online Unsupervised Domain Adaptation (SF-OUDA). We experimentally show that state-of-the-art methods have a rather limited ability to adapt pre-trained deep network models to unseen domains in an online manner. Our second contribution is an approach that relies on adaptive self-training and geometric-feature propagation to adapt a pre-trained source model online without requiring either source data or target labels. Our third contribution is to study SF-OUDA in a challenging setup where source data is synthetic and target data is point clouds captured in the real world. We use the recent SynLiDAR dataset as a synthetic source and introduce two new synthetic (source) datasets, which can stimulate future synthetic-to-real autonomous driving research. Our experiments show the effectiveness of our segmentation approach on thousands of real-world point clouds."
CoSMix: Compositional Semantic Mix for Domain Adaptation in 3D LiDAR Segmentation
Authors: Cristiano Saltori; Fabio Galasso; Giuseppe Fiameni; Nicu Sebe; Elisa Ricci; Fabio Poiesi
Venue: ECCV 2022 (main)

Abstract: "3D LiDAR semantic segmentation is fundamental for autonomous driving. Several Unsupervised Domain Adaptation (UDA) methods for point cloud data have been recently proposed to improve model generalization for different sensors and environments. Researchers working on UDA problems in the image domain have shown that sample mixing can mitigate domain shift. We propose a new approach of sample mixing for point cloud UDA, namely Compositional Semantic Mix (CoSMix), the first UDA approach for point cloud segmentation based on sample mixing. CoSMix consists of a two-branch symmetric network that can process labelled synthetic data (source) and real-world unlabelled point clouds (target) concurrently. Each branch operates on one domain by mixing selected pieces of data from the other one, and by using the semantic information derived from source labels and target pseudo-labels. We evaluate CoSMix on two large-scale datasets, showing that it outperforms state-of-the-art methods by a large margin."
A Unified Framework for Domain Adaptive Pose Estimation
Authors: Donghyun Kim; Kaihong Wang; Kate Saenko; Margrit Betke; Stan Sclaroff
Venue: ECCV 2022 (main)

Abstract: "While pose estimation is an important computer vision task, it requires expensive annotation and suffers from domain shift. In this paper, we investigate the problem of domain adaptive 2D pose estimation that transfers knowledge learned on a synthetic source domain to a target domain without supervision. While several domain adaptive pose estimation models have been proposed recently, they are not generic but only focus on either human pose or animal pose estimation, and thus their effectiveness is somewhat limited to specific scenarios. In this work, we propose a unified framework that generalizes well on various domain adaptive pose estimation problems. We propose to align representations using both input-level and output-level cues (pixels and pose labels, respectively), which facilitates the knowledge transfer from the source domain to the unlabeled target domain. Our experiments show that our method achieves state-of-the-art performance under various domain shifts. Our method outperforms existing baselines on human pose estimation by up to 4.5 percent points (pp), hand pose estimation by up to 7.4 pp, and animal pose estimation by up to 4.8 pp for dogs and 3.3 pp for sheep. These results suggest that our method is able to mitigate domain shift on diverse tasks and even unseen domains and objects (e.g., trained on horse and tested on dog). Our code will be publicly available at: https://github.com/VisionLearningGroup/UDA_PoseEstimation."