mtybilly/Instruct2DS huggingface dataset:
KVT: k-NN Attention for Boosting Vision Transformers
Authors: Xue Wang, Fan Wang, Ming Lin, Shuning Chang, Hao Li, Rong Jin Pichao Wang
Published in: European Conference on Computer Vision (ECCV), 2022 (Main Conference Track), 2022

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: Haoyan Guan, Aofan Jiang, Ya Zhang, Michael Spratling, Yan-Feng Wang Chaoqin Huang
Published in: European Conference on Computer Vision (ECCV), 2022 (Main Conference Track), 2022

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: David Stutz, Bernt Schiele Yong Guo
Published in: European Conference on Computer Vision (ECCV), 2022 (Main Conference Track), 2022

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: Kongming Liang, Ruoyi Du, Xian Sun, Zhanyu Ma, Jun Guo Tian Zhang
Published in: European Conference on Computer Vision (ECCV), 2022 (Main Conference Track), 2022

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: Nicu Sebe, Wei Wang Yue Song
Published in: European Conference on Computer Vision (ECCV), 2022 (Main Conference Track), 2022

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: Ruiyuan Gao, Qiang Xu Yijun Yang
Published in: European Conference on Computer Vision (ECCV), 2022 (Main Conference Track), 2022

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: Zhiqiang Shen, Yun Long, Eric Xing, Kwang-Ting Cheng, Chas Leichner Zechun Liu
Published in: European Conference on Computer Vision (ECCV), 2022 (Main Conference Track), 2022

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."
Learning from Multiple Annotator Noisy Labels via Sample-Wise Label Fusion
Authors: Fan-Keng Sun, Mingran Yang, Sucheng Ren, Zikai Xiong, Marc Engeler, Antonio Burazer, Linda Wildling, Luca Daniel, Duane S. Boning Zhengqi Gao
Published in: European Conference on Computer Vision (ECCV), 2022 (Main Conference Track), 2022

Abstract: "Data lies at the core of modern deep learning. The impressive performance of supervised learning is built upon a base of massive accurately labeled data. However, in some real-world applications, accurate labeling might not be viable; instead, multiple noisy labels (instead of one accurate label) are provided by several annotators for each data sample. Learning a classifier on such a noisy training dataset is a challenging task. Previous approaches usually assume that all data samples share the same set of parameters related to annotator errors, while we demonstrate that label error learning should be both annotator and data sample dependent. Motivated by this observation, we propose a novel learning algorithm. The proposed method displays superiority compared with several state-of-the-art baseline methods on MNIST, CIFAR-100, and ImageNet-100. Our code is available at: https://github.com/zhengqigao/Learning-from-Multiple-Annotator-Noisy-Labels."
Acknowledging the Unknown for Multi-Label Learning with Single Positive Labels
Authors: Pengfei Chen, Qiong Wang, Guangyong Chen, Pheng-Ann Heng Donghao Zhou
Published in: European Conference on Computer Vision (ECCV), 2022 (Main Conference Track), 2022

Abstract: "Due to the difficulty of collecting exhaustive multi-label annotations, multi-label datasets often contain partial labels. We consider an extreme of this weakly supervised learning problem, called single positive multi-label learning (SPML), where each multi-label training image has only one positive label. Traditionally, all unannotated labels are assumed as negative labels in SPML, which introduces false negative labels and causes model training to be dominated by assumed negative labels. In this work, we choose to treat all unannotated labels from an alternative perspective, i.e. acknowledging they are unknown. Hence, we propose entropy-maximization (EM) loss to attain a special gradient regime for providing proper supervision signals. Moreover, we propose asymmetric pseudo-labeling (APL), which adopts asymmetric-tolerance strategies and a self-paced procedure, to cooperate with EM loss and then provide more precise supervision. Experiments show that our method significantly improves performance and achieves state-of-the-art results on all four benchmarks. Code is available at https://github.com/Correr-Zhou/SPML-AckTheUnknown."
AutoMix: Unveiling the Power of Mixup for Stronger Classifiers
Authors: Siyuan Li, Di Wu, Zihan Liu, Zhiyuan Chen, Lirong Wu, Stan Z. Li Zicheng Liu
Published in: European Conference on Computer Vision (ECCV), 2022 (Main Conference Track), 2022

Abstract: "Data mixing augmentation have proved to be effective for improving the generalization ability of deep neural networks. While early methods mix samples by hand-crafted policies (\textit{e.g.}, linear interpolation), recent methods utilize saliency information to match the mixed samples and labels via complex offline optimization. However, there arises a trade-off between precise mixing policies and optimization complexity. To address this challenge, we propose a novel automatic mixup (AutoMix) framework, where the mixup policy is parameterized and serves the ultimate classification goal directly. Specifically, AutoMix reformulates the mixup classification into two sub-tasks (\textit{i.e.}, mixed sample generation and mixup classification) with corresponding sub-networks and solves them in a bi-level optimization framework. For the generation, a learnable lightweight mixup generator, Mix Block, is designed to generate mixed samples by modeling patch-wise relationships under the direct supervision of the corresponding mixed labels. To prevent the degradation and instability of bi-level optimization, we further introduce a momentum pipeline to train AutoMix in an end-to-end manner. Extensive experiments on nine image benchmarks prove the superiority of AutoMix compared with state-of-the-arts in various classification scenarios and downstream tasks."
MaxViT: Multi-axis Vision Transformer
Authors: Hossein Talebi, Han Zhang, Feng Yang, Peyman Milanfar, Alan Bovik, Yinxiao Li Zhengzhong Tu
Published in: European Conference on Computer Vision (ECCV), 2022 (Main Conference Track), 2022

Abstract: "Transformers have recently gained significant attention in the computer vision community. However, the lack of scalability of self-attention mechanisms with respect to image size has limited their wide adoption in state-of-the-art vision backbones. In this paper we introduce an efficient and scalable attention model we call multi-axis attention, which consists of two aspects: blocked local and dilated global attention. These design choices allow global-local spatial interactions on arbitrary input resolutions with only linear complexity. We also present a new architectural element by effectively blending our proposed attention model with convolutions, and accordingly propose a simple hierarchical vision backbone, dubbed MaxViT, by simply repeating the basic building block over multiple stages. Notably, MaxViT is able to ”see” globally throughout the entire network, even in earlier, high-resolution stages. We demonstrate the effectiveness of our model on a broad spectrum of vision tasks. On image classification, MaxViT achieves state-of-the-art performance under various settings: without extra data, MaxViT attains 86.5% ImageNet-1K top-1 accuracy; with ImageNet-21K pre-training, our model achieves 88.7% top-1 accuracy. For downstream tasks, MaxViT as a backbone delivers favorable performance on object detection as well as visual aesthetic assessment. We also show that our proposed model expresses strong generative modeling capability on ImageNet, demonstrating the superior potential of MaxViT blocks as a universal vision module. The source code and trained models will be available at https://github.com/google-research/maxvit."
ScalableViT: Rethinking the Context-Oriented Generalization of Vision Transformer
Authors: Hailong Ma, Jie Wu, Yansong Tang, Xuefeng Xiao, Min Zheng, Xiu Li Rui Yang
Published in: European Conference on Computer Vision (ECCV), 2022 (Main Conference Track), 2022

Abstract: "The vanilla self-attention mechanism inherently relies on pre-defined and steadfast computational dimensions. Such inflexibility restricts it from possessing context-oriented generalization that can bring more contextual cues and graphic representations. To mitigate this issue, we propose a Scalable Self-Attention (SSA) mechanism that leverages two scaling factors to release dimensions of query, key, and value matrices while unbinding them with the input. This scalability fetches context-oriented generalization and enhances object sensitivity, which pushes the whole network into a more effective trade-off state between precision and cost. Furthermore, we propose an Interactive Window-based Self-Attention (IWSA), which establishes interaction between non-overlapping regions by re-merging independent value tokens and aggregating spatial information from adjacent windows. By stacking the SSA and IWSA alternately, the Scalable Vision Transformer (ScalableViT) achieves state-of-the-art performance in general-purpose vision tasks. For example, ScalableViT-S outperforms Twins-SVT-S by 1.4% and Swin-T by 1.8% on ImageNet-1K classification."
Three Things Everyone Should Know about Vision Transformers
Authors: Matthieu Cord, Alaaeldin El-Nouby, Jakob Verbeek, Hervé Jégou Hugo Touvron
Published in: European Conference on Computer Vision (ECCV), 2022 (Main Conference Track), 2022

Abstract: "After their initial success in natural language processing, transformer architectures have rapidly gained traction in computer vision, providing state-of-the-art results for tasks such as image classification, detection, segmentation, and video analysis. We offer three insights based on simple and easy to implement variants of vision transformers. (1) The residual layers of vision transformers, which are usually processed sequentially, can to some extent be processed efficiently in parallel without noticeably affecting the accuracy. (2) Fine-tuning the weights of the attention layers is sufficient to adapt vision transformers to a higher resolution and to other classification tasks. This saves compute, reduces the peak memory consumption at fine-tuning time, and allows sharing the majority of weights across tasks. (3) Adding MLP-based patch pre-processing layers improves Bert-like self-supervised training based on patch masking. We evaluate the impact of these design choices using the ImageNet-1k dataset, and also report result on the ImageNet-v2 test set. Transfer performance is measured across six smaller datasets."
DeiT III: Revenge of the ViT
Authors: Matthieu Cord, Hervé Jégou Hugo Touvron
Published in: European Conference on Computer Vision (ECCV), 2022 (Main Conference Track), 2022

Abstract: "A Vision Transformer (ViT) is a simple neural architecture amenable to serve several computer vision tasks. It has limited built-in architectural priors, in contrast to more recent architectures that incorporate priors either about the input data or of specific tasks. Recent works show that ViTs benefit from self-supervised pre-training, in particular BerT-like pre-training like BeiT. In this paper, we revisit the supervised training of ViTs. Our procedure builds upon and simplifies a recipe introduced for training ResNet-50. It includes a new simple data-augmentation procedure with only 3 augmentations, closer to the practice in self-supervised learning. Our evaluations on Image classification (ImageNet-1k with and without pre-training on ImageNet-21k), transfer learning and semantic segmentation show that our procedure outperforms by a large margin previous fully supervised training recipes for ViT. It also reveals that the performance of our ViT trained with supervision is comparable to that of more recent architectures. Our results could serve as better baselines for recent self-supervised approaches demonstrated on ViT."
MixSKD: Self-Knowledge Distillation from Mixup for Image Recognition
Authors: Zhulin An, Helong Zhou, Linhang Cai, Xiang Zhi, Jiwen Wu, Yongjun Xu, Qian Zhang Chuanguang Yang
Published in: European Conference on Computer Vision (ECCV), 2022 (Main Conference Track), 2022

Abstract: "Unlike the conventional Knowledge Distillation (KD), Self-KD allows a network to learn knowledge from itself without any guidance from extra networks. This paper proposes to perform Self-KD from image Mixture (MixSKD), which integrates these two techniques into a unified framework. MixSKD mutually distills feature maps and probability distributions between the random pair of original images and their mixup images in a meaningful way. Therefore, it guides the network to learn cross-image knowledge by modelling supervisory signals from mixup images. Moreover, we construct a self-teacher network by aggregating multi-stage feature maps for providing soft labels to supervise the backbone classifier, further improving the efficacy of self-boosting. Experiments on image classification and transfer learning to object detection and semantic segmentation demonstrate that MixSKD outperforms other state-of-the-art Self-KD and data augmentation methods. The code is available at https://github.com/winycg/Self-KD-Lib."
Self-Feature Distillation with Uncertainty Modeling for Degraded Image Recognition
Authors: Weisheng Dong, Xin Li, Jinjian Wu, Leida Li, Guangming Shi Zhou Yang
Published in: European Conference on Computer Vision (ECCV), 2022 (Main Conference Track), 2022

Abstract: "Despite the remarkable performance on high-quality (HQ) data, the accuracy of deep image recognition models degrades rapidly in the presence of low-quality (LQ) images. Both feature de-drifting and quality agnostic models have been developed to make the features extracted from degraded images closer to those of HQ images. In these methods, the L2-norm is usually used as a constraint. It treats each pixel in the feature equally and may result in relatively poor reconstruction performance in some difficult regions. To address this issue, we propose a novel self-feature distillation method with uncertainty modeling for better producing HQ-like features from low-quality observations in this paper. Specifically, in a standard recognition model, we use the HQ features to distill the corresponding degraded ones and conduct uncertainty modeling according to the diversity of degradation sources to adaptively increase the weights of feature regions that are difficult to recover in the distillation loss. Experiments demonstrate that our method can extract HQ-like features better even when the inputs are degraded images, which makes the model more robust than other approaches."
Novel Class Discovery without Forgetting
Authors: Sujoy Paul, Gaurav Aggarwal, Soma Biswas, Piyush Rai, Kai Han, Vineeth N Balasubramanian K J Joseph
Published in: European Conference on Computer Vision (ECCV), 2022 (Main Conference Track), 2022

Abstract: "Humans possess an innate ability to identify and differentiate instances that they are not familiar with, by leveraging and adapting the knowledge that they have acquired so far. Importantly, they achieve this without deteriorating the performance on their earlier learning. Inspired by this, we identify and formulate a new, pragmatic problem setting of NCDwF: Novel Class Discovery without Forgetting, which tasks a machine learning model to incrementally discover novel categories of instances from unlabeled data, while maintaining its performance on the previously seen categories. We propose 1) a method to generate pseudo-latent representations which act as a proxy for (no longer available) labeled data, thereby alleviating forgetting, 2) a mutual-information based regularizer which enhances unsupervised discovery of novel classes, and 3) a simple Known Class Identifier which aids generalized inference when the testing data contains instances form both seen and unseen categories. We introduce experimental protocols based on CIFAR-10, CIFAR-100 and ImageNet-1000 to measure the trade-off between knowledge retention and novel class discovery. Our extensive evaluations reveal that existing models catastrophically forget previously seen categories while identifying novel categories, while our method is able to effectively balance between the competing objectives. We hope our work will attract further research into this newly identified pragmatic problem setting."
SAFA: Sample-Adaptive Feature Augmentation for Long-Tailed Image Classification
Authors: Jianfu Zhang, Zhongyi Sun, Ke Yan Yan Hong
Published in: European Conference on Computer Vision (ECCV), 2022 (Main Conference Track), 2022

Abstract: "Imbalanced datasets with long-tailed distribution widely exist in practice, posing great challenges for deep networks on how to handle the biased predictions between head (majority, frequent) classes and tail (minority, rare) classes. Feature space of tail classes learned by deep networks is usually under-represented, causing heterogeneous performance among different classes. Existing methods augment tail-class features to compensate tail classes on feature space, but these methods fail to generalize on test phase. To mitigate this problem, we propose a novel Sample-Adaptive Feature Augmentation (SAFA) to augment features for tail classes resulting in ameliorating the classifier performance. SAFA aims to extract diverse and transferable semantic directions from head classes, and adaptively translate tail-class features along extracted semantic directions for augmentation. SAFA leverages a recycling training scheme ensuring augmented features are sample-specific. Contrastive loss ensures the transferable semantic directions are class-irrelevant and mode seeking loss is adopted to produce diverse tail-class features and enlarge the feature space of tail classes. The proposed SAFA as a plug-in is convenient and versatile to be combined with different methods during training phase without additional computational burden at test time. By leveraging SAFA, we obtain outstanding results on CIFAR-LT-10, CIFAR-LT-100, Places-LT, ImageNet-LT, and iNaturalist2018."
Negative Samples Are at Large: Leveraging Hard-Distance Elastic Loss for Re-identification
Authors: Sungmin Eum, Heesung Kwon Hyungtae Lee
Published in: European Conference on Computer Vision (ECCV), 2022 (Main Conference Track), 2022

Abstract: "We present a Momentum Re-identification (MoReID) framework that can leverage a very large number of negative samples in training for general re-identification task. The design of this framework is inspired by Momentum Contrast (MoCo), which uses a dictionary to store current and past batches to build a large set of encoded samples. As we find it less effective to use past positive samples which may be highly inconsistent to the encoded feature property formed with the current positive samples, MoReID is designed to use only a large number of negative samples stored in the dictionary. However, if we train the model using the widely used Triplet loss that uses only one sample to represent a set of positive/negative samples, it is hard to effectively leverage the enlarged set of negative samples acquired by the MoReID framework. To maximize the advantage of using the scaled-up negative sample set, we newly introduce Hard-distance Elastic loss (HE loss), which is capable of using more than one hard sample to represent a large number of samples. Our experiments demonstrate that a large number of negative samples provided by MoReID framework can be utilized at full capacity only with the HE loss, achieving the state-of-the-art accuracy on three re-ID benchmarks, VeRi-776, Market-1501, and VeRi-Wild."
Discrete-Constrained Regression for Local Counting Models
Authors: Angela Yao Haipeng Xiong
Published in: European Conference on Computer Vision (ECCV), 2022 (Main Conference Track), 2022

Abstract: "Local counts, or the number of objects in a local area, is a continuous value by nature. Yet recent state-of-the-art methods show that formulating counting as a classification task performs better than regression. Through a series of experiments on carefully controlled synthetic data, we show that this counter-intuitive result is caused by imprecise ground truth local counts. Factors such as biased dot annotations and incorrectly matched Gaussian kernels used to generate ground truth counts introduce deviations from the true local counts. Standard continuous regression is highly sensitive to these errors, explaining the performance gap between classification and regression. To mitigate the sensitivity, we loosen the regression formulation from a continuous scale to a discrete ordering and propose a novel discrete-constrained (DC) regression. Applied to crowd counting, DC-regression is more accurate than both classification and standard regression on three public benchmarks. A similar advantage also holds for the age estimation task, verifying the overall effectiveness of DC-regression. Code is available at https://github.com/xhp-hust-2018-2011/dcreg."
Breadcrumbs: Adversarial Class-Balanced Sampling for Long-Tailed Recognition
Authors: Haoxiang Li, Hao Kang, Gang Hua, Nuno Vasconcelos Bo Liu
Published in: European Conference on Computer Vision (ECCV), 2022 (Main Conference Track), 2022

Abstract: "The problem of long-tailed recognition, where the number of examples per class is highly unbalanced, is considered. While training with class-balanced sampling has been shown effective for this problem, it is known to over-fit to few-shot classes. It is hypothesized that this is due to the repeated sampling of examples and can be addressed by feature space augmentation. A new feature augmentation strategy, EMANATE, based on back-tracking of features across epochs during training, is proposed. It is shown that, unlike class-balanced sampling, this is an adversarial augmentation strategy. A new sampling procedure, Breadcrumb, is then introduced to implement adversarial class-balanced sampling without extra computation. Experiments on three popular long-tailed recognition datasets show that Breadcrumb training produces classifiers that outperform existing solutions to the problem."
Chairs Can Be Stood On: Overcoming Object Bias in Human-Object Interaction Detection
Authors: Yangyang Guo, Yongkang Wong, Mohan Kankanhalli Guangzhi Wang
Published in: European Conference on Computer Vision (ECCV), 2022 (Main Conference Track), 2022

Abstract: "Detecting Human-Object Interaction (HOI) in images is an important step towards high-level visual comprehension. Existing work often shed light on improving either human and object detection, or interaction recognition. However, due to the limitation of datasets, these methods tend to fit well on frequent interactions conditioned on the detected objects, yet largely ignoring the rare ones, which is referred to as the object bias problem in this paper. In this work, we for the first time, uncover the problem from two aspects: unbalanced interaction distribution and biased model learning. To overcome the object bias problem, we propose a novel plug-and-play Object-wise Debiasing Memory (ODM) method for re-balancing the distribution of interactions under detected objects. Equipped with carefully designed read and write strategies, the proposed ODM allows rare interaction instances to be more frequently sampled for training, thereby alleviating the object bias induced by the unbalanced interaction distribution. We apply this method to three advanced baselines and conduct experiments on the HICO-DET and HOI-COCO datasets. To quantitatively study the object bias problem, we advocate a new protocol for evaluating model performance. As demonstrated in the experimental results, our method brings consistent and significant improvements over baselines, especially on rare interactions under each object. In addition, when evaluating under the conventional standard setting, our method achieves new state-of-the-art on the two benchmarks."
A Fast Knowledge Distillation Framework for Visual Recognition
Authors: Eric Xing Zhiqiang Shen
Published in: European Conference on Computer Vision (ECCV), 2022 (Main Conference Track), 2022

Abstract: "While Knowledge Distillation (KD) has been recognized as a useful tool in many visual tasks, such as supervised classification and self-supervised representation learning, the main drawback of a vanilla KD framework is its mechanism that consumes the majority of the computational overhead on forwarding through the giant teacher networks, making the entire learning procedure inefficient and costly. The recently proposed solution ReLabel suggests creating a label map for the entire image. During training, it receives the cropped region-level label by RoI aligning on a pre-generated entire label map, which allows for efficient supervision generation without having to pass through the teachers repeatedly. However, as the pre-trained teacher employed in ReLabel is from the conventional multi-crop scheme, there are various mismatches between the global label-map and region-level labels in this technique, resulting in performance deterioration compared to the vanilla KD. In this study, we present a Fast Knowledge Distillation (FKD) framework that replicates the distillation training phase and generates soft labels using the multi-crop KD approach, meanwhile training faster than ReLabel since no post-processes such as RoI align and softmax operations are used. When conducting multi-crop in the same image for data loading, our FKD is even more efficient than the traditional image classification framework. On ImageNet-1K, we obtain 80.1% Top-1 accuracy on ResNet-50, outperforming ReLabel by 1.2% while being faster in training and more flexible to use. On the distillation-based self-supervised learning task, we also show that FKD has an efficiency advantage. Code and models are available at: https://github.com/szq0214/FKD."
DICE: Leveraging Sparsification for Out-of-Distribution Detection
Authors: Yixuan Li Yiyou Sun
Published in: European Conference on Computer Vision (ECCV), 2022 (Main Conference Track), 2022

Abstract: "Detecting out-of-distribution (OOD) inputs is a central challenge for safely deploying machine learning models in the real world. Previous methods commonly rely on an OOD score derived from the overparameterized weight space, while largely overlooking the role of sparsification. In this paper, we reveal important insights that reliance on unimportant weights and units can directly attribute to the brittleness of OOD detection. To mitigate the issue, we propose a sparsification-based OOD detection framework termed DICE. Our key idea is to rank weights based on a measure of contribution, and selectively use the most salient weights to derive the output for OOD detection. We provide both empirical and theoretical insights, characterizing and explaining the mechanism by which DICE improves OOD detection. By pruning away noisy signals, DICE provably reduces the output variance for OOD data, resulting in a sharper output distribution and stronger separability from ID data. We demonstrate the effectiveness of sparsification-based OOD detection on several benchmarks and establish competitive performance."
Invariant Feature Learning for Generalized Long-Tailed Classification
Authors: Mingyuan Tao, Jiaxin Qi, Zhenguang Liu, Hanwang Zhang Kaihua Tang
Published in: European Conference on Computer Vision (ECCV), 2022 (Main Conference Track), 2022

Abstract: "Existing long-tailed classification (LT) methods only focus on tackling the class-wise imbalance that head classes have more samples than tail classes, but overlook the attribute-wise imbalance. In fact, even if the class is balanced, samples within each class may still be long-tailed due to the varying attributes. Note that the latter is fundamentally more ubiquitous and challenging than the former because attributes are not just implicit for most datasets, but also combinatorially complex, thus prohibitively expensive to be balanced. Therefore, we introduce a novel research problem: Generalized Long-Tailed classification (GLT), to jointly consider both kinds of imbalances. By “generalized”, we mean that a GLT method should naturally solve the traditional LT, but not vice versa. Not surprisingly, we find that most class-wise LT methods degenerate in our proposed two benchmarks: ImageNet-GLT and MSCOCO-GLT. We argue that it is because they over-emphasize the adjustment of class distribution while neglecting to learn attribute-invariant features. To this end, we propose an Invariant Feature Learning (IFL) method as the first strong baseline for GLT. IFL first discovers environments with divergent intra-class distributions from the imperfect predictions, and then learns invariant features across them. Promisingly, as an improved feature backbone, IFL boosts all the LT line-up: one/two-stage re-balance, augmentation, and ensemble. Codes and benchmarks are available on Github: https://github.com/KaihuaTang/Generalized-Long-Tailed-Benchmarks.pytorch"
Sliced Recursive Transformer
Authors: Zechun Liu, Eric Xing Zhiqiang Shen
Published in: European Conference on Computer Vision (ECCV), 2022 (Main Conference Track), 2022

Abstract: "We present a neat yet effective recursive operation on vision transformers that can improve parameter utilization without involving additional parameters. This is achieved by sharing weights across the depth of transformer networks. The proposed method can obtain a substantial gain ( 2%) simply using naive recursive operation, requires no special or sophisticated knowledge for designing principles of networks, and introduces minimal computational overhead to the training procedure. To reduce the additional computation caused by recursive operation while maintaining the superior accuracy, we propose an approximating method through multiple sliced group self-attentions across recursive layers which can reduce the cost consumption by 10 30% without sacrificing performance. We call our model Sliced Recursive Transformer (SReT), a novel and parameter-efficient vision transformer design that is compatible with a broad range of other designs for efficient ViT architectures. Our best model establishes significant improvement on ImageNet-1K over state-of-the-art methods while containing fewer parameters. The proposed weight sharing mechanism by sliced recursion structure allows us to build a transformer with more than 100 or even 1000 shared layers with ease while keeping a compact size (13 15M), to avoid optimization difficulties when the model is too large. The flexible scalability has shown great potential for scaling up models and constructing extremely deep vision transformers. Code is available at https://github.com/szq0214/SReT."
Cross-Domain Ensemble Distillation for Domain Generalization
Authors: Sungyeon Kim, Suha Kwak Kyungmoon Lee
Published in: European Conference on Computer Vision (ECCV), 2022 (Main Conference Track), 2022

Abstract: "Domain generalization is the task of learning models that generalize to unseen target domains. We propose a simple yet effective method for domain generalization, named cross-domain ensemble distillation (XDED), that learns domain-invariant features while encouraging the model to converge to flat minima, which recently turned out to be a sufficient condition for domain generalization. To this end, our method generates an ensemble of the output logits from training data with the same label but from different domains and then penalizes each output for the mismatch with the ensemble. Also, we present a de-stylization technique that standardizes features to encourage the model to produce style-consistent predictions even in an arbitrary target domain. Our method greatly improves generalization capability in public benchmarks for cross-domain image classification, cross-dataset person re-ID, and cross-dataset semantic segmentation. Moreover, we show that models learned by our method are robust against adversarial attacks and unseen corruptions."
Centrality and Consistency: Two-Stage Clean Samples Identification for Learning with Instance-Dependent Noisy Labels
Authors: Guanbin Li, Yipeng Qin, Feng Liu, Yizhou Yu Ganlong Zhao
Published in: European Conference on Computer Vision (ECCV), 2022 (Main Conference Track), 2022

Abstract: "Deep models trained with noisy labels are prone to over-fitting and struggle in generalization. Most existing solutions are based on an ideal assumption that the label noise is class-conditional, i.e., instances of the same class share the same noise model, and are independent of features. While in practice, the real-world noise patterns are usually more fine-grained as instance-dependent ones, which poses a big challenge, especially in the presence of inter-class imbalance. In this paper, we propose a two-stage clean samples identification method to address the aforementioned challenge. First, we employ a class-level feature clustering procedure for the early identification of clean samples that are near the class-wise prediction centers. Notably, we address the class imbalance problem by aggregating rare classes according to their prediction entropy. Second, for the remaining clean samples that are close to the ground truth class boundary (usually mixed with the samples with instance-dependent noises), we propose a novel consistency-based classification method that identifies them using the consistency of two classifier heads: the higher the consistency, the larger the probability that a sample is clean. Extensive experiments on several challenging benchmarks demonstrate the superior performance of our method against the state-of-the-art."
Hyperspherical Learning in Multi-Label Classification
Authors: Yunquan Zhu, Mengtian Li, Xiujun Shu, Ruizhi Qiao, Bo Ren Bo Ke
Published in: European Conference on Computer Vision (ECCV), 2022 (Main Conference Track), 2022

Abstract: "Learning from online data with noisy web labels is gaining more attention due to the increasing cost of fully annotated datasets in large-scale multi-label classification tasks. Partial (positive) annotated data, as a particular case of data with noisy labels, are economically accessible. And they serve as benchmarks to evaluate the learning capacity of state-of-the-art methods in real scenarios, though they contain a large number of samples with false negative labels. Existing (partial) multi-label methods are usually studied in the Euclidean space, where the relationship between the label embeddings and image features is not symmetrical and thus can be challenging to learn. To alleviate this problem, we propose reformulating the task into a hyperspherical space, where an angular margin can be incorporated into a hyperspherical multi-label loss function. This margin allows us to effectively balance the impact of false negative and true positive labels. We further design a mechanism to tune the angular margin and scale adaptively. We investigate the effectiveness of our method under three multi-label scenarios (single positive labels, partial positive labels and full labels) on four datasets (VOC12, COCO, CUB-200 and NUS-WIDE). In the single and partial positive labels scenarios, our method achieves state-of-the-art performance. The robustness of our method is verified by comparing the performances at different proportions of partial positive labels in the datasets. Our method also obtains more than 1\% improvement over the BCE loss even on the fully annotated scenario. Analysis shows that the learned label embeddings potentially correspond to actual label correlation, since in hyperspherical space label embeddings and image features are symmetrical and interchangeable. This further indicates the geometric interpretability of our method. Code is available at https://github.com/TencentYoutuResearch/MultiLabel-HML."
When Active Learning Meets Implicit Semantic Data Augmentation
Authors: Jin Zhang, Pan Wang, Jie Chen, Jianqiang Li Zhuangzhuang Chen
Published in: European Conference on Computer Vision (ECCV), 2022 (Main Conference Track), 2022

Abstract: "Active learning (AL) is a label-efficient technique for training deep models when only a limited labeled set is available and the manual annotation is expensive. Implicit semantic data augmentation (ISDA) effectively extends the limited amount of labeled samples and increases the diversity of labeled sets without introducing a noticeable extra computational cost. The scarcity of labeled instances and the huge annotation cost of unlabelled samples encourage us to ponder on the combination of AL and ISDA. A nature direction is a pipelined integration, which selects the unlabeled samples via acquisition function in AL for labeling and generates virtual samples by changing the selected samples to semantic transformation directions within ISDA. However, this pipelined combination would not guarantee the diversity of virtual samples. This paper proposes diversity-aware semantic transformation active learning, or DAST-AL framework, that looks ahead the effect of ISDA in the selection of unlabeled samples. Specifically, DAST-AL exploits expected partial model change maximization (EPMCM) to consider selected samples’ potential contribution of the diversity to the labeled set by leveraging the semantic transformation within ISDA when selecting the unlabeled samples. After that, DAST-AL can confidently and efficiently augment the labeled set by implicitly generating more diverse samples. The empirical results on both image classification and semantic segmentation tasks show that the proposed DAST-AL can slightly outperform the state-of-the-art AL approaches. Under the same condition, the proposed method takes less than 3 minutes for the first cycle of active labeling while the existing agreement discrepancy selection incurs more than 40 minutes."
VL-LTR: Learning Class-Wise Visual-Linguistic Representation for Long-Tailed Visual Recognition
Authors: Wenhai Wang, Xizhou Zhu, Jifeng Dai, Yu Qiao Changyao Tian
Published in: European Conference on Computer Vision (ECCV), 2022 (Main Conference Track), 2022

Abstract: "Recently, computer vision foundation models such as CLIP and ALI-GN, have shown impressive generalization capabilities on various downstream tasks. But their abilities to deal with the long-tailed data still remain to be proved. In this work, we present a novel framework based on pre-trained visual-linguistic models for long-tailed recognition (LTR), termed VL-LTR, and conduct empirical studies on the benefits of introducing text modality for long-tailed recognition tasks. Compared to existing approaches, the proposed VL-LTR has the following merits. (1) Our method can not only learn visual representation from images but also learn corresponding linguistic representation from noisy class-level text descriptions collected from the Internet; (2) Our method can effectively use the learned visual-linguistic representation to improve the visual recognition performance, especially for classes with fewer image samples. We also conduct extensive experiments and set the new state-of-the-art performance on widely-used LTR benchmarks. Notably, our method achieves 77.2\% overall accuracy on ImageNet-LT, which significantly outperforms the previous best method by over 17 points, and is close to the prevailing performance training on the full ImageNet. Code shall be released."