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Learning Imbalanced Data with vision Transformers

Learning Imbalanced Data with Vision Transformers

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ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Xu, Zhengzhuo Liu, Ruikang Yang, Shuo Chai, Zenghao Yuan, Chun Tsinghua Univ Shenzhen Int Grad Sch Beijing Peoples R China

ISBN: (纸本)9798350301298

The real-world data tends to be heavily imbalanced and severely skew the data-driven deep neural networks, which makes Long-Tailed recognition (LTR) a massive challenging task. Existing LTR methods seldom train vision Transformers (ViTs) with Long-Tailed (LT) data, while the off-the-shelf pretrain weight of ViTs always leads to unfair comparisons. In this paper, we systematically investigate the ViTs' performance in LTR and propose LiVT to train ViTs from scratch only with LT data. With the observation that ViTs suffer more severe LTR problems, we conduct Masked Generative Pretraining (MGP) to learn generalized features. With ample and solid evidence, we show that MGP is more robust than supervised manners. Although Binary Cross Entropy (BCE) loss performs well with ViTs, it struggles on the LTR tasks. We further propose the balanced BCE to ameliorate it with strong theoretical groundings. Specially, we derive the unbiased extension of Sigmoid and compensate extra logit margins for deploying it. Our Bal-BCE contributes to the quick convergence of ViTs in just a few epochs. Extensive experiments demonstrate that with MGP and Bal-BCE, LiVT successfully trains ViTs well without any additional data and outperforms comparable state-of-the-art methods significantly, e.g., our ViT-B achieves 81.0% Top-1 accuracy in iNaturalist 2018 without bells and whistles. Code is available at https://***/XuZhengzhuo/LiVT.

关键词： continual low-shot meta or long-tail learning Transfer

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Image Quality-aware Diagnosis via Meta-knowledge Co-embedding

Image Quality-aware Diagnosis via Meta-knowledge Co-embeddin...

引用

ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Che, Haoxuan Chen, Siyu Chen, Hao Hong Kong Univ Sci & Technol Hong Kong Peoples R China

ISBN: (纸本)9798350301298

Medical images usually suffer from image degradation in clinical practice, leading to decreased performance of deep learning-based models. To resolve this problem, most previous works have focused on filtering out degradation-causing low-quality images while ignoring their potential value for models. Through effectively learning and leveraging the knowledge of degradations, models can better resist their adverse effects and avoid misdiagnosis. In this paper, we raise the problem of image quality-aware diagnosis, which aims to take advantage of low-quality images and image quality labels to achieve a more accurate and robust diagnosis. However, the diversity of degradations and superficially unrelated targets between image quality assessment and disease diagnosis makes it still quite challenging to effectively leverage quality labels to assist diagnosis. Thus, to tackle these issues, we propose a novel meta-knowledge co-embedding network, consisting of two subnets: Task Net and Meta Learner. Task Net constructs an explicit quality information utilization mechanism to enhance diagnosis via knowledge co-embedding features, while Meta Learner ensures the effectiveness and constrains the semantics of these features via meta-learning and joint-encoding masking. Superior performance on five datasets with four widely-used medical imaging modalities demonstrates the effectiveness and generalizability of our method.

关键词： cell microscopy Medical and biological vision

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Learning Accurate 3D Shape Based on Stereo Polarimetric Imaging

Learning Accurate 3D Shape Based on Stereo Polarimetric Imag...

引用

ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Huang, Tianyu Li, Haoang He, Kejing Sui, Congying Li, Bin Liu, Yun-Hui Chinese Univ Hong Kong Hong Kong Peoples R China Tech Univ Munich Munich Germany

ISBN: (纸本)9798350301298

Shape from Polarization (SfP) aims to recover surface normal using the polarization cues of light. The accuracy of existing SfP methods is affected by two main problems. First, the ambiguity of polarization cues partially results in false normal estimation. Second, the widely-used assumption about orthographic projection is too ideal. To solve these problems, we propose the first approach that combines deep learning and stereo polarization information to recover not only normal but also disparity. Specifically, for the ambiguity problem, we design a Shape Consistency-based Mask Prediction (SCMP) module. It exploits the inherent consistency between normal and disparity to identify the areas with false normal estimation. We replace the unreliable features enclosed by these areas with new features extracted by global attention mechanism. As to the orthographic projection problem, we propose a novel Viewing Direction-aided Positional Encoding (VDPE) strategy. This strategy is based on the unique pixel-viewing direction encoding, and thus enables our neural network to handle the non-orthographic projection. In addition, we establish a real-world stereo SfP dataset that contains various object categories and illumination conditions. Experiments showed that compared with existing SfP methods, our approach is more accurate. Moreover, our approach shows higher robustness to light variation.

关键词： Physics-based vision and shape-from-X

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MaskCLIP: Masked Self-Distillation Advances Contrastive Language-Image Pretraining

MaskCLIP: Masked Self-Distillation Advances Contrastive Lang...

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ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Dong, Xiaoyi Bao, Jianmin Zheng, Yinglin Zhang, Ting Chen, Dongdong Yang, Hao Zeng, Ming Zhang, Weiming Yuan, Lu Chen, Dong Wen, Fang Yu, Nenghai Univ Sci & Technol China Hefei Anhui Peoples R China Microsoft Res Asia Beijing Peoples R China Xiamen Univ Xiamen Peoples R China Microsoft Cloud AI Orlando FL USA

ISBN: (纸本)9798350301298

This paper presents a simple yet effective framework MaskCLIP, which incorporates a newly proposed masked self-distillation into contrastive language-image pretraining. The core idea of masked self-distillation is to distill representation from a full image to the representation predicted from a masked image. Such incorporation enjoys two vital benefits. First, masked self-distillation targets local patch representation learning, which is complementary to vision-language contrastive focusing on text-related representation. Second, masked self-distillation is also consistent with vision-language contrastive from the perspective of training objective as both utilize the visual encoder for feature aligning, and thus is able to learn local semantics getting indirect supervision from the language. We provide specially designed experiments with a comprehensive analysis to validate the two benefits. Symmetrically, we also introduce the local semantic supervision into the text branch, which further improves the pretraining performance. With extensive experiments, we show that MaskCLIP, when applied to various challenging downstream tasks, achieves superior results in linear probing, finetuning, and zero-shot performance with the guidance of the language encoder. Code will be release at https://***/LightDXY/MaskCLIP.

关键词： detection recognition: Categorization retrieval

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Multi-view Adversarial Discriminator: Mine the Non-causal Factors for Object Detection in Unseen Domains

Multi-view Adversarial Discriminator: Mine the Non-causal Fa...

引用

ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Xu, Mingjun Qin, Lingyun Chen, Weijie Pu, Shiliang Zhang, Lei Chongqing Univ Sch Microelect & Commun Engn Chongqing Peoples R China Hikvision Res Inst Hangzhou Peoples R China

ISBN: (纸本)9798350301298

Domain shift degrades the performance of object detection models in practical applications. To alleviate the influence of domain shift, plenty of previous work try to decouple and learn the domain-invariant (common) features from source domains via domain adversarial learning (DAL). However, inspired by causal mechanisms, we find that previous methods ignore the implicit insignificant non-causal factors hidden in the common features. This is mainly due to the single-view nature of DAL. In this work, we present an idea to remove non-causal factors from common features by multi-view adversarial training on source domains, because we observe that such insignificant non-causal factors may still be significant in other latent spaces (views) due to the multi-mode structure of data. To summarize, we propose a Multi-view Adversarial Discriminator (MAD) based domain generalization model, consisting of a Spurious Correlations Generator (SCG) that increases the diversity of source domain by random augmentation and a Multi-View Domain Classifier (MVDC) that maps features to multiple latent spaces, such that the non-causal factors are removed and the domain-invariant features are purified. Extensive experiments on six benchmarks show our MAD obtains state-of-the-art performance.

关键词： detection recognition: Categorization retrieval

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Efficient Frequency Domain-based Transformers for High-Quality Image Deblurring

Efficient Frequency Domain-based Transformers for High-Quali...

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ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Kong, Lingshun Dong, Jiangxin Ge, Jianjun Li, Mingqiang Pan, Jinshan Nanjing Univ Sci & Technol Sch Comp Sci & Engn Nanjing Peoples R China China Elect Technol Grp Corp Informat Sci Acad Beijing Peoples R China

ISBN: (纸本)9798350301298

We present an effective and efficient method that explores the properties of Transformers in the frequency domain for high-quality image deblurring. Our method is motivated by the convolution theorem that the correlation or convolution of two signals in the spatial domain is equivalent to an element-wise product of them in the frequency domain. This inspires us to develop an efficient frequency domain-based self-attention solver (FSAS) to estimate the scaled dot-product attention by an element-wise product operation instead of the matrix multiplication in the spatial domain. In addition, we note that simply using the naive feed-forward network (FFN) in Transformers does not generate good deblurred results. To overcome this problem, we propose a simple yet effective discriminative frequency domain-based FFN (DFFN), where we introduce a gated mechanism in the FFN based on the Joint Photographic Experts Group (JPEG) compression algorithm to discriminatively determine which low- and high-frequency information of the features should be preserved for latent clear image restoration. We formulate the proposed FSAS and DFFN into an asymmetrical network based on an encoder and decoder architecture, where the FSAS is only used in the decoder module for better image deblurring. Experimental results show that the proposed method performs favorably against the state-of-the-art approaches.

关键词： Low-level vision

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DualRel: Semi-Supervised Mitochondria Segmentation from A Prototype Perspective

DualRel: Semi-Supervised Mitochondria Segmentation from A Pr...

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ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Mai, Huayu Sun, Rui Zhang, Tianzhu Xiong, Zhiwei Wu, Feng Univ Sci & Technol China Langfang Peoples R China Hefei Comprehens Natl Sci Ctr Inst Artificial Intelligence Hefei Peoples R China Deep Space Explorat Lab Hefei Peoples R China

ISBN: (纸本)9798350301298

Automatic mitochondria segmentation enjoys great popularity with the development of deep learning. However, existing methods rely heavily on the labor-intensive manual gathering by experienced domain experts. And naively applying semi-supervised segmentation methods in the natural image field to mitigate the labeling cost is undesirable. In this work, we analyze the gap between mitochondrial images and natural images and rethink how to achieve effective semi-supervised mitochondria segmentation, from the perspective of reliable prototype-level supervision. We propose a novel end-to-end dual-reliable (DualRel) network, including a reliable pixel aggregation module and a reliable prototype selection module. The proposed DualRel enjoys several merits. First, to learn the prototypes well without any explicit supervision, we carefully design the referential correlation to rectify the direct pairwise correlation. Second, the reliable prototype selection module is responsible for further evaluating the reliability of prototypes in constructing prototype-level consistency regularization. Extensive experimental results on three challenging benchmarks demonstrate that our method performs favorably against state-of-the-art semi-supervised segmentation methods. Importantly, with extremely few samples used for training, DualRel is also on par with current state-of-the-art fully supervised methods.

关键词： cell microscopy Medical and biological vision

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LASP: Text-to-Text Optimization for Language-Aware Soft Prompting of vision & Language Models

LASP: Text-to-Text Optimization for Language-Aware Soft Prom...

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ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Bulat, Adrian Tzimiropoulos, Georgios Samsung AI Cambridge Toronto ON Canada Tech Univ Iasi Iasi Romania Qucen Maty Univ London London England

ISBN: (纸本)9798350301298

Soft prompt learning has recently emerged as one of the methods of choice for adapting V&L models to a downstream task using a few training examples. However, current methods significantly overfit the training data, suffering from large accuracy degradation when tested on unseen classes from the same domain. To this end, in this paper, we make the following 4 contributions: (1) To alleviate base class overfitting, we propose a novel Language-Aware Soft Prompting (LASP) learning method by means of a text-to-text cross-entropy loss that maximizes the probability of the learned prompts to be correctly classified with respect to pre-defined hand-crafted textual prompts. (2) To increase the representation capacity of the prompts, we propose grouped LASP where each group of prompts is optimized with respect to a separate subset of textual prompts. (3) We identify a visual-language misalignment introduced by prompt learning and LASP, and more importantly, propose a re-calibration mechanism to address it. (4) We show that LASP is inherently amenable to including, during training, virtual classes, i.e. class names for which no visual samples are available, further increasing the robustness of the learned prompts. Through evaluations on 11 datasets, we show that our approach (a) significantly outperforms all prior works on soft prompting, and (b) matches and surpasses, for the first time, the accuracy on novel classes obtained by hand-crafted prompts and CLIP for 8 out of 11 test datasets. Code will be made available here.

关键词： continual low-shot meta or long-tail learning Transfer

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Instance Relation Graph Guided Source-Free Domain Adaptive Object Detection

Instance Relation Graph Guided Source-Free Domain Adaptive O...

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ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Vibashan, V. S. Oza, Poojan Patel, Vishal M. Johns Hopkins Univ Baltimore MD 21205 USA

ISBN: (纸本)9798350301298

Unsupervised Domain Adaptation (UDA) is an effective approach to tackle the issue of domain shift. Specifically, UDA methods try to align the source and target representations to improve generalization on the target domain. Further, UDA methods work under the assumption that the source data is accessible during the adaptation process. However, in real-world scenarios, the labelled source data is often restricted due to privacy regulations, data transmission constraints, or proprietary data concerns. The Source-Free Domain Adaptation (SFDA) setting aims to alleviate these concerns by adapting a source-trained model for the target domain without requiring access to the source data. In this paper, we explore the SFDA setting for the task of adaptive object detection. To this end, we propose a novel training strategy for adapting a source-trained object detector to the target domain without source data. More precisely, we design a novel contrastive loss to enhance the target representations by exploiting the objects relations for a given target domain input. These object instance relations are modelled using an Instance Relation Graph (IRG) network, which are then used to guide the contrastive representation learning. In addition, we utilize a student-teacher to effectively distill knowledge from source-trained model to target domain. Extensive experiments on multiple object detection benchmark datasets show that the proposed approach is able to efficiently adapt source-trained object detectors to the target domain, outperforming state-of-the-art domain adaptive detection methods. Code and models are provided in https://***/irg-sfda-web/.

关键词： detection recognition: Categorization retrieval

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Are Binary Annotations Sufficient? Video Moment Retrieval via Hierarchical Uncertainty-based Active Learning

Are Binary Annotations Sufficient? Video Moment Retrieval vi...

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ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Ji, Wei Liang, Renjie Zheng, Zhedong Zhang, Wenqiao Zhang, Shengyu Li, Juncheng Li, Mengze Chua, Tat-seng Natl Univ Singapore Singapore Singapore Zhejiang Univ Hangzhou Peoples R China

ISBN: (纸本)9798350301298

Recent research on video moment retrieval has mostly focused on enhancing the performance of accuracy, efficiency, and robustness, all of which largely rely on the abundance of high-quality annotations. While the precise frame-level annotations are time-consuming and cost-expensive, few attentions have been paid to the labeling process. In this work, we explore a new interactive manner to stimulate the process of human-in-the-loop annotation in video moment retrieval task. The key challenge is to select "ambiguous" frames and videos for binary annotations to facilitate the network training. To be specific, we propose a new hierarchical uncertainty-based modeling that explicitly considers modeling the uncertainty of each frame within the entire video sequence corresponding to the query description, and selecting the frame with the highest uncertainty. Only selected frame will be annotated by the human experts, which can largely reduce the workload. After obtaining a small number of labels provided by the expert, we show that it is sufficient to learn a competitive video moment retrieval model in such a harsh environment. Moreover, we treat the uncertainty score of frames in a video as a whole, and estimate the difficulty of each video, which can further relieve the burden of video selection. In general, our active learning strategy for video moment retrieval works not only at the frame level but also at the sequence level. Experiments on two public datasets validate the effectiveness of our proposed method. Our code is released at https://***/renjie-liang/HUAL.

关键词： vision applications and systems

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