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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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Activating More Pixels in Image Super-Resolution Transformer

Activating More Pixels in Image Super-Resolution Transformer

引用

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

作者： Chen, Xiangyu Wang, Xintao Zhou, Jiantao Qiao, Yu Dong, Chao Univ Macau State Key Lab Internet Things Smart City Zhuhai Peoples R China Chinese Acad Sci Shenzhen Key Lab Comp Vis & Pattern Recognit Shenzhen Inst Adv Technol Beijing Peoples R China Shanghai Artificial Intelligence Lab Shanghai Peoples R China Tencent PCG ARC Lab Shenzhen Peoples R China

ISBN: (纸本)9798350301298

Transformer-based methods have shown impressive performance in low-level vision tasks, such as image super-resolution. However, we find that these networks can only utilize a limited spatial range of input information through attribution analysis. This implies that the potential of Transformer is still not fully exploited in existing networks. In order to activate more input pixels for better reconstruction, we propose a novel Hybrid Attention Transformer (HAT). It combines both channel attention and window-based self-attention schemes, thus making use of their complementary advantages of being able to utilize global statistics and strong local fitting capability. Moreover, to better aggregate the cross-window information, we introduce an overlapping cross-attention module to enhance the interaction between neighboring window features. In the training stage, we additionally adopt a same-task pre-training strategy to exploit the potential of the model for further improvement. Extensive experiments show the effectiveness of the proposed modules, and we further scale up the model to demonstrate that the performance of this task can be greatly improved. Our overall method significantly outperforms the state-of-the-art methods by more than 1dB.

关键词： Low-level vision

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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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Learning Accurate 3D Shape Based on Stereo Polarimetric Imaging

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

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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...

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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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GAPartNet: Cross-Category Domain-Generalizable Object Perception and Manipulation via Generalizable and Actionable Parts

GAPartNet: Cross-Category Domain-Generalizable Object Percep...

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

作者： Geng, Haoran Xu, Helin Zhao, Chengyang Xu, Chao Yi, Li Huang, Siyuan Wang, He Peking Univ CFCS Beijing Peoples R China Peking Univ Sch EECS Beijing Peoples R China Beijing Inst Gen Artificial Intelligence Beijing Peoples R China Tsinghua Univ Beijing Peoples R China Univ Calif Los Angeles Los Angeles CA USA

ISBN: (纸本)9798350301298

For years, researchers have been devoted to generalizable object perception and manipulation, where cross-category generalizability is highly desired yet underexplored. In this work, we propose to learn such cross-category skills via Generalizable and Actionable Parts (GAParts). By identifying and defining 9 GAPart classes (lids, handles, etc.) in 27 object categories, we construct a large-scale part-centric interactive dataset, GAPartNet, where we provide rich, part-level annotations (semantics, poses) for 8,489 part instances on 1,166 objects. Based on GAPartNet, we investigate three cross-category tasks: part segmentation, part pose estimation, and part-based object manipulation. Given the significant domain gaps between seen and unseen object categories, we propose a robust 3D segmentation method from the perspective of domain generalization by integrating adversarial learning techniques. Our method outperforms all existing methods by a large margin, no matter on seen or unseen categories. Furthermore, with part segmentation and pose estimation results, we leverage the GAPart pose definition to design part-based manipulation heuristics that can generalize well to unseen object categories in both the simulator and the real world.

关键词： Embodied vision: Active agents simulation

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