GenAI4ELab/papercli-papers huggingface dataset:
Locality Guidance for Improving Vision Transformers on Tiny Datasets
Authors: Kehan Li; Runyi Yu; Zhennan Wang; Li Yuan; Guoli Song; Jie Chen
Venue: ECCV 2022 (main)

Abstract: "While the Vision Transformer (VT) architecture is becoming trendy in computer vision, pure VT models perform poorly on tiny datasets. To address this issue, this paper proposes the locality guidance for improving the performance of VTs on tiny datasets. We first analyze that the local information, which is of great importance for understanding images, is hard to be learned with limited data due to the high flexibility and intrinsic globality of the self-attention mechanism in VTs. To facilitate local information, we realize the locality guidance for VTs by imitating the features of an already trained convolutional neural network (CNN), inspired by the built-in local-to-global hierarchy of CNN. Under our dual-task learning paradigm, the locality guidance provided by a lightweight CNN trained on low-resolution images is adequate to accelerate the convergence and improve the performance of VTs to a large extent. Therefore, our locality guidance approach is very simple and efficient, and can serve as a basic performance enhancement method for VTs on tiny datasets. Extensive experiments demonstrate that our method can significantly improve VTs when training from scratch on tiny datasets and is compatible with different kinds of VTs and datasets. For example, our proposed method can boost the performance of various VTs on tiny datasets (e.g., 13.07% for DeiT, 8.98% for T2T and 7.85% for PVT), and enhance even stronger baseline PVTv2 by 1.86% to 79.30%, showing the potential of VTs on tiny datasets. The code is available at https://github.com/lkhl/tiny-transformers."
Neighborhood Collective Estimation for Noisy Label Identification and Correction
Authors: Jichang Li; Guanbin Li; Feng Liu; Yizhou Yu
Venue: ECCV 2022 (main)

Abstract: "Learning with noisy labels (LNL) aims at designing strategies to improve model performance and generalization by mitigating the effects of model overfitting to noisy labels. The key success of LNL lies in identifying as many clean samples as possible from massive noisy data, while rectifying the wrongly assigned noisy labels. Recent advances employ the predicted label distributions of individual samples to perform noise verification and noisy label correction, easily giving rise to confirmation bias. To mitigate this issue, we propose Neighborhood Collective Estimation, in which the predictive reliability of a candidate sample is re-estimated by contrasting it against its feature-space nearest neighbors. Specifically, our method is divided into two steps: 1) Neighborhood Collective Noise Verification to separate all training samples into a clean or noisy subset, 2) Neighborhood Collective Label Correction to relabel noisy samples, and then auxiliary techniques are used to assist further model optimization. Extensive experiments on four commonly used benchmark datasets, i.e., CIFAR-10, CIFAR-100, Clothing-1M and Webvision-1.0, demonstrate that our proposed method considerably outperforms state-of-the-art methods."
Few-Shot Class-Incremental Learning via Entropy-Regularized Data-Free Replay
Authors: Huan Liu; Li Gu; Zhixiang Chi; Yang Wang; Yuanhao Yu; Jun Chen; Jin Tang
Venue: ECCV 2022 (main)

Abstract: "Few-shot class-incremental learning (FSCIL) has been proposed aiming to enable a deep learning system to incrementally learn new classes with limited data. Recently, a pioneer claims that the commonly used replay-based method in class-incremental learning (CIL) is ineffective and thus not preferred for FSCIL. This has, if truth, a significant influence on the fields of FSCIL. In this paper, we show through empirical results that adopting the data replay is surprisingly favorable. However, storing and replaying old data can lead to a privacy concern. To address this issue, we alternatively propose using data-free replay that can synthesize data by a generator without accessing real data. In observing the the effectiveness of uncertain data for knowledge distillation, we impose entropy regularization in the generator training to encourage more uncertain examples. Moreover, we propose to relabel the generated data with one-hot-like labels. This modification allows the network to learn by solely minimizing the cross-entropy loss, which mitigates the problem of balancing different objectives in the conventional knowledge distillation approach. Finally, we show extensive experimental results and analysis on CIFAR-100, miniImageNet and CUB-200 to demonstrate the effectiveness of our proposed one."
Anti-Retroactive Interference for Lifelong Learning
Authors: Runqi Wang; Yuxiang Bao; Baochang Zhang; Jianzhuang Liu; Wentao Zhu; Guodong Guo
Venue: ECCV 2022 (main)

Abstract: "Humans can continuously learn new knowledge. However, machine learning models suffer from drastic dropping in performance on previous tasks after learning new tasks. Cognitive science points out that the competition of similar knowledge is an important cause of forgetting. In this paper, we design a paradigm for lifelong learning based on meta-learning and associative mechanism of the brain. It tackles the problem from two aspects: extracting knowledge and memorizing knowledge. First, we disrupt the sample’s background distribution through a background attack, which strengthens the model to extract the key features of each task. Second, according to the similarity between incremental knowledge and base knowledge, we design an adaptive fusion of incremental knowledge, which helps the model allocate capacity to the knowledge of different difficulties. It is theoretically analyzed that the proposed learning paradigm can make the models of different tasks converge to the same optimum. The proposed method is validated on the MNIST, CIFAR100, CUB200 and ImageNet100 datasets. The code is available at https://github.com/bhrqw/ARI."
Towards Calibrated Hyper-Sphere Representation via Distribution Overlap Coefficient for Long-Tailed Learning
Authors: Hualiang Wang; Siming Fu; Xiaoxuan He; Hangxiang Fang; Zuozhu Liu; Haoji Hu
Venue: ECCV 2022 (main)

Abstract: "Long-tailed learning aims to tackle the crucial challenge that head classes dominate the training procedure under severe class imbalance in real-world scenarios. However, little attention has been given to how to quantify the dominance severity of head classes in the representation space. Motivated by this, we generalize the cosine-based classifiers to a von Mises-Fisher (vMF) mixture model, denoted as vMF classifier, which enables to quantitatively measure representation quality upon the hyper-sphere space via calculating distribution overlap coefficient. To our knowledge, this is the first work to measure the representation quality of classifiers and features from the perspective of the distribution overlap coefficient. On top of it, we formulate the inter-class discrepancy and class feature consistency loss terms to alleviate the interference among the classifier weights and align features with classifier weights. Furthermore, a novel post-training calibration algorithm is devised to zero-costly boost the performance via inter-class overlap coefficients. Our models outperform previous work with a large margin and achieve state-of-the-art performance on long-tailed image classification, semantic segmentation, and instance segmentation tasks (e.g., we achieve 55.0% overall accuracy with ResNetXt-50 in ImageNet-LT). Our code is available at https://github.com/VipaiLab/vMF_OP."
Dynamic Metric Learning with Cross-Level Concept Distillation
Authors: Wenzhao Zheng; Yuanhui Huang; Borui Zhang; Jie Zhou; Jiwen Lu
Venue: ECCV 2022 (main)

Abstract: "A good similarity metric should be consistent with the human perception of similarities: a sparrow is more similar to an owl if compared to a dog but is more similar to a dog if compared to a car. It depends on the semantic levels to determine if two images are from the same class. As most existing metric learning methods push away interclass samples and pull closer intraclass samples, it seems contradictory if the labels cross semantic levels. The core problem is that a negative pair on a finer semantic level can be a positive pair on a coarser semantic level, so pushing away this pair damages the class structure on the coarser semantic level. We identify the negative repulsion as the key obstacle in existing methods since a positive pair is always positive for coarser semantic levels but not for negative pairs. Our solution, cross-level concept distillation (CLCD), is simple in concept: we only pull closer positive pairs. To facilitate the cross-level semantic structure of the image representations, we propose a hierarchical concept refiner to construct multiple levels of concept embeddings of an image and then pull closer the distance of the corresponding concepts. Extensive experiments demonstrate that the proposed CLCD method outperforms all other competing methods on the hierarchically labeled datasets."
MENet: A Memory-Based Network with Dual-Branch for Efficient Event Stream Processing
Authors: Linhui Sun; Yifan Zhang; Ke Cheng; Jian Cheng; Hanqing Lu
Venue: ECCV 2022 (main)

Abstract: "Event cameras are bio-inspired sensors that asynchronously capture per-pixel brightness change and trigger a stream of events instead of frame-based images. Each event stream is generally split into multiple sliding windows for subsequent processing. However, most existing event-based methods ignore the motion continuity between adjacent spatiotemporal windows, which will result in the loss of dynamic information and additional computational costs. To efficiently extract strong features for event streams containing dynamic information, this paper proposes a novel memory-based network with dual-branch, namely MENet. It contains a base branch with a full-sized event point-wise processing structure to extract the base features and an incremental branch equipped with a light-weighted network to capture the temporal dynamics between two adjacent spatiotemporal windows. For enhancing the features, especially in the incremental branch, a point-wise memory bank is designed, which sketches the representative information of event feature space. Compared with the base branch, the incremental branch reduces the computational complexity up to 5 times and improves the speed by 19 times. Experiments show that MENet significantly reduces the computational complexity compared with previous methods while achieving state-of-the-art performance on gesture recognition and object recognition."
Out-of-Distribution Detection with Boundary Aware Learning
Authors: Sen Pei; Xin Zhang; Bin Fan; Gaofeng Meng
Venue: ECCV 2022 (main)

Abstract: "There is an increasing need to determine whether inputs are out-of-distribution (OOD) for safely deploying machine learning models in the open world scenario. Typical neural classifiers are based on the closed-world assumption, where the training data and the test data are drawn i.i.d. from the same distribution, and as a result, give over-confident predictions even faced with OOD inputs. For tackling this problem, previous studies either use real outliers for training or generate synthetic OOD data under strong assumptions, which are either costly or intractable to generalize. In this paper, we propose boundary aware learning (BAL), a novel framework that can learn the distribution of OOD features adaptively. The key idea of BAL is to generate OOD features from trivial to hard progressively with a generator, meanwhile, a discriminator is trained for distinguishing these synthetic OOD features and in-distribution (ID) features. Benefiting from the adversarial training scheme, the discriminator can well separate ID and OOD features, allowing more robust OOD detection. The proposed BAL achieves state-of-the-art performance on classification benchmarks, reducing up to 13.9% FPR95 compared with previous methods."
Learning Hierarchy Aware Features for Reducing Mistake Severity
Authors: Ashima Garg; Depanshu Sani; Saket Anand
Venue: ECCV 2022 (main)

Abstract: "Label hierarchies are often available apriori as part of biological taxonomy or language datasets WordNet. Several works exploit these to learn hierarchy aware features in order to improve the classifier to make semantically meaningful mistakes while maintaining or reducing the overall error. In this paper, we propose a novel approach for learning Hierarchy Aware Features (HAF) that leverages classifiers at each level of the hierarchy that are constrained to generate predictions consistent with the label hierarchy. The classifiers are trained by minimizing a Jensen-Shannon Divergence with target soft labels obtained from the fine-grained classifiers. Additionally, we employ a simple geometric loss that constrains the feature space geometry to capture the semantic structure of the label space. HAF is a training time approach that improves the mistakes while maintaining top-1 error, thereby, addressing the problem of cross-entropy loss that treats all mistakes as equal. We evaluate HAF on three hierarchical datasets and achieve state-of-the-art results on the iNaturalist-19 and CIFAR-100 datasets."
Learning to Detect Every Thing in an Open World
Authors: Kuniaki Saito; Ping Hu; Trevor Darrell; Kate Saenko
Venue: ECCV 2022 (main)

Abstract: "Many open-world applications require the detection of novel objects, yet state-of-the-art object detection and instance segmentation networks do not excel at this task. The key issue lies in their assumption that regions without any annotations should be suppressed as negatives, which teaches the model to treat any unannotated (hidden) objects as background. To address this issue, we propose a simple yet surprisingly powerful data augmentation and training scheme we call Learning to Detect Every Thing (LDET). To avoid suppressing hidden objects, we develop a new data augmentation method, BackErase, which pastes annotated objects on a background image sampled from a small region of the original image. Since training solely on such synthetically-augmented images suffers from domain shift, we propose a multi-domain training strategy that allows the model to generalize to real images. LDET leads to significant improvements on many datasets in the open-world instance segmentation task, outperforming baselines on cross-category generalization on COCO, as well as cross-dataset evaluation on UVO, Objects365, and Cityscapes."
KVT: k-NN Attention for Boosting Vision Transformers
Authors: Pichao Wang; Xue Wang; Fan Wang; Ming Lin; Shuning Chang; Hao Li; Rong Jin
Venue: ECCV 2022 (main)

Abstract: "Convolutional Neural Networks (CNNs) have dominated computer vision for years, due to its ability in capturing locality and translation invariance. Recently, many vision transformer architectures have been proposed and they show promising performance. A key component in vision transformers is the fully-connected self-attention which is more powerful than CNNs in modelling long range dependencies. However, since the current dense self-attention uses all image patches (tokens) to compute attention matrix, it may neglect locality of images patches and involve noisy tokens (e.g., clutter background and occlusion), leading to a slow training process and potential degradation of performance. To address these problems, we propose the $k$-NN attention for boosting vision transformers. Specifically, instead of involving all the tokens for attention matrix calculation, we only select the top-$k$ similar tokens from the keys for each query to compute the attention map. The proposed $k$-NN attention naturally inherits the local bias of CNNs without introducing convolutional operations, as nearby tokens tend to be more similar than others. In addition, the $k$-NN attention allows for the exploration of long range correlation and at the same time filters out irrelevant tokens by choosing the most similar tokens from the entire image. Despite its simplicity, we verify, both theoretically and empirically, that $k$-NN attention is powerful in speeding up training and distilling noise from input tokens. Extensive experiments are conducted by using 11 different vision transformer architectures to verify that the proposed $k$-NN attention can work with any existing transformer architectures to improve its prediction performance. The codes are available at \url{https://github.com/damo-cv/KVT}."
Registration Based Few-Shot Anomaly Detection
Authors: Chaoqin Huang; Haoyan Guan; Aofan Jiang; Ya Zhang; Michael Spratling; Yan-Feng Wang
Venue: ECCV 2022 (main)

Abstract: "This paper considers few-shot anomaly detection (FSAD), a practical yet under-studied setting for anomaly detection (AD), where only a limited number of normal images are provided for each category at training. So far, existing FSAD studies follow the one-model-per-category learning paradigm used for standard AD, and the inter-category commonality has not been explored. Inspired by how humans detect anomalies, i.e., comparing an image in question to normal images, we here leverage registration, an image alignment task that is inherently generalizable across categories, as the proxy task, to train a category-agnostic anomaly detection model. During testing, the anomalies are identified by comparing the registered features of the test image and its corresponding support (normal) images. As far as we know, this is the first FSAD method that trains a single generalizable model and requires no re-training or parameter fine-tuning for new categories. Experimental results have shown that the proposed method outperforms the state-of-the-art FSAD methods by 3%-8% in AUC on the MVTec and MPDD benchmarks. Source code is available at: https://github.com/MediaBrain-SJTU/RegAD"
Improving Robustness by Enhancing Weak Subnets
Authors: Yong Guo; David Stutz; Bernt Schiele
Venue: ECCV 2022 (main)

Abstract: "Despite their success, deep networks have been shown to be highly susceptible to perturbations, often causing significant drops in accuracy. In this paper, we investigate model robustness on perturbed inputs by studying the performance of internal sub-networks (subnets). Interestingly, we observe that most subnets show particularly poor robustness against perturbations. More importantly, these weak subnets are correlated with the overall lack of robustness. Tackling this phenomenon, we propose a new training procedure that identifies and enhances weak subnets (EWS) to improve robustness. Specifically, we develop a search algorithm to find particularly weak subnets and explicitly strengthen them via knowledge distillation from the full network. We show that EWS greatly improves both robustness against corrupted images as well as accuracy on clean data. Being complementary to popular data augmentation methods, EWS consistently improves robustness when combined with these approaches. To highlight the flexibility of our approach, we combine EWS also with popular adversarial training methods resulting in improved adversarial robustness."
Learning Invariant Visual Representations for Compositional Zero-Shot Learning
Authors: Tian Zhang; Kongming Liang; Ruoyi Du; Xian Sun; Zhanyu Ma; Jun Guo
Venue: ECCV 2022 (main)

Abstract: "Compositional Zero-Shot Learning (CZSL) aims to recognize novel compositions using knowledge learned from seen attribute-object compositions in the training set. Previous works mainly project an image and a composition into a common embedding space to measure their compatibility score. However, both attributes and objects share the visual representations learned above, leading the model to exploit spurious correlations and bias towards seen pairs. Instead, we reconsider CZSL as an out-of-distribution generalization problem. If an object is treated as a domain, we can learn object-invariant features to recognize the attributes attached to any object reliably. Similarly, attribute-invariant features can also be learned when recognizing the objects with attributes as domains. Specifically, we propose an invariant feature learning framework to align different domains at the representation and gradient levels to capture the intrinsic characteristics associated with the tasks. Experiments on two CZSL benchmarks demonstrate that the proposed method significantly outperforms the previous state-of-the-art."
Improving Covariance Conditioning of the SVD Meta-Layer by Orthogonality
Authors: Yue Song; Nicu Sebe; Wei Wang
Venue: ECCV 2022 (main)

Abstract: "Inserting an SVD meta-layer into neural networks is prone to make the covariance ill-conditioned, which could harm the model in the training stability and generalization abilities. In this paper, we systematically study how to improve the covariance conditioning by enforcing orthogonality to the Pre-SVD layer. Existing orthogonal treatments on the weights are first investigated. However, these techniques can improve the conditioning but would hurt the performance. To avoid such a side effect, we propose the Nearest Orthogonal Gradient (NOG) and Optimal Learning Rate (OLR). The effectiveness of our methods is validated in two applications: decorrelated Batch Normalization (BN) and Global Covariance Pooling (GCP). Extensive experiments on visual recognition demonstrate that our methods can simultaneously improve the covariance conditioning and generalization. Moreover, the combinations with orthogonal weight can further boost the performances."
Out-of-Distribution Detection with Semantic Mismatch under Masking
Authors: Yijun Yang; Ruiyuan Gao; Qiang Xu
Venue: ECCV 2022 (main)

Abstract: "This paper proposes a novel out-of-distribution (OOD) detection framework named MOODCat for image classifiers. MOODCat masks a random portion of the input image and uses a generative model to synthesize the masked image to a new image conditioned on the classification result. It then calculates the semantic difference between the original image and the synthesized one for OOD detection. Compared to existing solutions, MOODCat naturally learns the semantic information of the in-distribution data with the proposed mask and conditional synthesis strategy, which is critical to identify OODs. Experimental results demonstrate that MOODCat outperforms state-of-the-art OOD detection solutions by a large margin. Our code is available at https://github.com/cure-lab/MOODCat."
Data-Free Neural Architecture Search via Recursive Label Calibration
Authors: Zechun Liu; Zhiqiang Shen; Yun Long; Eric Xing; Kwang-Ting Cheng; Chas Leichner
Venue: ECCV 2022 (main)

Abstract: "This paper aims to explore the feasibility of neural architecture search (NAS) given only a pre-trained model without using any original training data. This is an important circumstance for privacy protection, bias avoidance, etc., in real-world scenarios. To achieve this, we start by synthesizing usable data through recovering the knowledge from a pre-trained deep neural network. Then we use the synthesized data and their predicted soft labels to guide NAS. We identify that the quality of the synthesized data will substantially affect the NAS results. Particularly, we find NAS requires the synthesized images to possess enough semantics, diversity, and a minimal domain gap from the natural images. To meet these requirements, we propose recursive label calibration to encode more relative semantics in images, as well as regional update strategy to enhance the diversity. Further, we use input and feature-level regularization to mimic the original data distribution in latent space and reduce the domain gap. We instantiate our proposed framework with three popular NAS algorithms: DARTS, ProxylessNAS and SPOS. Surprisingly, our results demonstrate that the architectures discovered by searching with our synthetic data achieve accuracy that is comparable to, or even higher than, architectures discovered by searching from the original ones, for the first time, deriving the conclusion that NAS can be done effectively with no need of access to the original or called natural data if the synthesis method is well designed. Code and models are availabel at: https://github.com/liuzechun/Data-Free-NAS."