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MAESTER: Masked Autoencoder Guided Segmentation at Pixel Resolution for Accurate, Self-Supervised Subcellular Structure recognition

MAESTER: Masked Autoencoder Guided Segmentation at Pixel Res...

引用

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

作者： Xie, Ronald Pang, Kuan Bader, Gary D. Wang, Bo Univ Toronto Toronto ON M5S 1A1 Canada Vector Inst Toronto ON Canada Univ Hlth Network Toronto ON Canada Donnelly Ctr Toronto ON Canada

ISBN: (纸本)9798350301298

Accurate segmentation of cellular images remains an elusive task due to the intrinsic variability in morphology of biological structures. Complete manual segmentation is unfeasible for large datasets, and while supervised methods have been proposed to automate segmentation, they often rely on manually generated ground truths which are especially challenging and time consuming to generate in biology due to the requirement of domain expertise. Furthermore, these methods have limited generalization capacity, requiring additional manual labels to be generated for each dataset and use case. We introduce MAESTER (Masked AutoEncoder guided SegmenTation at pixEl Resolution), a self-supervised method for accurate, subcellular structure segmentation at pixel resolution. MAESTER treats segmentation as a representation learning and clustering problem. Specifically, MAESTER learns semantically meaningful token representations of multi-pixel image patches while simultaneously maintaining a sufficiently large field of view for contextual learning. We also develop a cover-and-stride inference strategy to achieve pixel-level subcellular structure segmentation. We evaluated MAESTER on a publicly available volumetric electron microscopy (VEM) dataset of primary mouse pancreatic islets beta cells and achieved upwards of 29.1% improvement over state-of-the-art under the same evaluation criteria. Furthermore, our results are competitive against supervised methods trained on the same tasks, closing the gap between self-supervised and supervised approaches. MAESTER shows promise for alleviating the critical bottleneck of ground truth generation for imaging related data analysis and thereby greatly increasing the rate of biological discovery. Code available at https : / / github. com / bowang-lab/MAESTER

关键词： cell microscopy Medical and biological vision

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CiCo: Domain-Aware Sign Language Retrieval via Cross-Lingual Contrastive Learning

CiCo: Domain-Aware Sign Language Retrieval via Cross-Lingual...

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

作者： Cheng, Yiting Wei, Fangyun Bao, Jianmin Chen, Dong Zhang, Wenqiang Fudan Univ Sch Comp Sci Shanghai Peoples R China Microsoft Res Asia Beijing Peoples R China

ISBN: (纸本)9798350301298

This work focuses on sign language retrieval-a recently proposed task for sign language understanding. Sign language retrieval consists of two sub-tasks: text-to-sign-video (T2V) retrieval and sign-video-to-text (V2T) retrieval. Different from traditional video-text retrieval, sign language videos, not only contain visual signals but also carry abundant semantic meanings by themselves due to the fact that sign languages are also natural languages. Considering this character, we formulate sign language retrieval as a cross-lingual retrieval problem as well as a video-text retrieval task. Concretely, we take into account the linguistic properties of both sign languages and natural languages, and simultaneously identify the fine-grained cross-lingual (i.e., sign-to-word) mappings while contrasting the texts and the sign videos in a joint embedding space. This process is termed as cross-lingual contrastive learning. Another challenge is raised by the data scarcity issue-sign language datasets are orders of magnitude smaller in scale than that of speech recognition. We alleviate this issue by adopting a domain-agnostic sign encoder pre-trained on large-scale sign videos into the target domain via pseudo-labeling. Our framework, termed as domain-aware sign language retrieval via Cross-lingual Contrastive learning or CiCo for short, outperforms the pioneering method by large margins on various datasets, e.g., +22.4 T2V and +28.0 V2T R@1 improvements on How2Sign dataset, and +13.7 T2V and +17.1 V2T R@1 improvements on PHOENIX-2014T dataset. Code and models are available at: https://***/FangyunWei/SLRT.

关键词： vision applications and systems

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Distilling Cross-Temporal Contexts for Continuous Sign Language recognition

Distilling Cross-Temporal Contexts for Continuous Sign Langu...

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

作者： Guo, Leming Xue, Wanli Guo, Qing Liu, Bo Zhang, Kaihua Yuan, Tiantian Chen, Shengyong Tianjin Univ Technol Sch Comp Sci & Engn Tianjin Peoples R China ASTAR Ctr Frontier AI Res Singapore Singapore Walmart Global Tech Sunnyvale CA USA Nanjing Univ Informat Sci & Technol Sch Comp & Software Nanjing Peoples R China Tianjin Univ Technol Tech Coll Deaf Tianjin Peoples R China

ISBN: (纸本)9798350301298

Continuous sign language recognition (CSLR) aims to recognize glosses in a sign language video. State-of-the-art methods typically have two modules, a spatial perception module and a temporal aggregation module, which are jointly learned end-to-end. Existing results in [9,20,25,36] have indicated that, as the frontal component of the overall model, the spatial perception module used for spatial feature extraction tends to be insufficiently trained. In this paper, we first conduct empirical studies and show that a shallow temporal aggregation module allows more thorough training of the spatial perception module. However, a shallow temporal aggregation module cannot well capture both local and global temporal context information in sign language. To address this dilemma, we propose a cross-temporal context aggregation (CTCA) model. Specifically, we build a dual-path network that contains two branches for perceptions of local temporal context and global temporal context. We further design a cross-context knowledge distillation learning objective to aggregate the two types of context and the linguistic prior. The knowledge distillation enables the resultant one-branch temporal aggregation module to perceive local-global temporal and semantic context. This shallow temporal perception module structure facilitates spatial perception module learning. Extensive experiments on challenging CSLR benchmarks demonstrate that our method outperforms all state-of-the-art methods.

关键词： body gesture Humans: Face movement pose

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An Erudite Fine-Grained Visual Classification Model

An Erudite Fine-Grained Visual Classification Model

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

作者： Chang, Dongliang Tong, Yujun Du, Ruoyi Hospedales, Timothy Song, Yi-Zhe Ma, Zhanyu Beijing Univ Posts & Telecommunicat Beijing Peoples R China Univ Edinburgh Edinburgh Midlothian Scotland Univ Surrey SketchX CVSSP Surrey England

ISBN: (纸本)9798350301298

Current fine-grained visual classification (FGVC) models are isolated. In practice, we first need to identify the coarse-grained label of an object, then select the corresponding FGVC model for recognition. This hinders the application of FGVC algorithms in real-life scenarios. In this paper, we propose an erudite FGVC model jointly trained by several different datasets(1), which can efficiently and accurately predict an object's fine-grained label across the combined label space. We found through a pilot study that positive and negative transfers co-occur when different datasets are mixed for training, i.e., the knowledge from other datasets is not always useful. Therefore, we first propose a feature disentanglement module and a feature re-fusion module to reduce negative transfer and boost positive transfer between different datasets. In detail, we reduce negative transfer by decoupling the deep features through many dataset-specific feature extractors. Subsequently, these are channel-wise re-fused to facilitate positive transfer. Finally, we propose a meta-learning based dataset-agnostic spatial attention layer to take full advantage of the multi-dataset training data, given that localisation is dataset-agnostic between different datasets. Experimental results across 11 different mixed-datasets built on four different FGVC datasets demonstrate the effectiveness of the proposed method. Furthermore, the proposed method can be easily combined with existing FGVC methods to obtain state-of-the-art results. Our code is available at https:// ***/PRIS-CV/An-Erudite-FGVC-Model.

关键词： detection recognition: Categorization retrieval

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Discriminator-Cooperated Feature Map Distillation for GAN Compression

Discriminator-Cooperated Feature Map Distillation for GAN Co...

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

作者： Hu, Tie Lin, Mingbao You, Lizhou Chao, Fei Ji, Rongrong Xiamen Univ Sch Informat MAC Lab Xiamen Peoples R China Xiamen Univ Inst Artificial Intelligence Xiamen Xiamen Peoples R China Tencent Youtu Lab Shanghai Peoples R China Xiamen Univ Shenzhen Res Inst Xiamen Peoples R China

ISBN: (纸本)9798350301298

Despite excellent performance in image generation, Generative Adversarial Networks (GANs) are notorious for its requirements of enormous storage and intensive computation. As an awesome "performance maker", knowledge distillation is demonstrated to be particularly efficacious in exploring low-priced GANs. In this paper, we investigate the irreplaceability of teacher discriminator and present an inventive discriminator-cooperated distillation, abbreviated as DCD, towards refining better feature maps from the generator. In contrast to conventional pixel-to-pixel match methods in feature map distillation, our DCD utilizes teacher discriminator as a transformation to drive intermediate results of the student generator to be perceptually close to corresponding outputs of the teacher generator. Furthermore, in order to mitigate mode collapse in GAN compression, we construct a collaborative adversarial training paradigm where the teacher discriminator is from scratch established to co-train with student generator in company with our DCD. Our DCD shows superior results compared with existing GAN compression methods. For instance, after reducing over 40x MACs and 80x parameters of CycleGAN, we well decrease FID metric from 61.53 to 48.24 while the current SoTA method merely has 51.92. This work's source code has been made accessible at https://***/poopit/DCD-official.

关键词： Efficient and scalable vision

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CDDFuse: Correlation-Driven Dual-Branch Feature Decomposition for Multi-Modality Image Fusion

CDDFuse: Correlation-Driven Dual-Branch Feature Decompositio...

引用

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

作者： Zhao, Zixiang Bai, Haowen Zhang, Jiangshe Zhang, Yulun Xu, Shuang Lin, Zudi Timofte, Radu Van Gool, Luc Xi An Jiao Tong Univ Xian Peoples R China Swiss Fed Inst Technol Comp Vis Lab Zurich Switzerland Northwestern Polytech Univ Shenzhen Inst Res & Dev Shenzhen Peoples R China Northwestern Polytech Univ Xian Peoples R China Harvard Univ Cambridge England Univ Wurzburg Wurzburg Germany

ISBN: (纸本)9798350301298

Multi-modality (MM) image fusion aims to render fused images that maintain the merits of different modalities, e.g., functional highlight and detailed textures. To tackle the challenge in modeling cross-modality features and decomposing desirable modality-specific and modality-shared features, we propose a novel Correlation-Driven feature Decomposition Fusion (CDDFuse) network. Firstly, CDDFuse uses Restormer blocks to extract cross-modality shallow features. We then introduce a dual-branch Transformer-CNN feature extractor with Lite Transformer (LT) blocks leveraging long-range attention to handle low-frequency global features and Invertible Neural Networks (INN) blocks focusing on extracting high-frequency local information. A correlation-driven loss is further proposed to make the low-frequency features correlated while the high-frequency features un-correlated based on the embedded information. Then, the LT-based global fusion and INN-based local fusion layers output the fused image. Extensive experiments demonstrate that our CDDFuse achieves promising results in multiple fusion tasks, including infrared-visible image fusion and medical image fusion. We also show that CDDFuse can boost the performance in downstream infrared-visible semantic segmentation and object detection in a unified benchmark. The code is available at https://***/Zhaozixiang1228/MMIF-CDDFuse.

关键词： Low-level vision

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LidarGait: Benchmarking 3D Gait recognition with Point Clouds

LidarGait: Benchmarking 3D Gait Recognition with Point Cloud...

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

作者： Shen, Chuanfu Chao, Fan Wu, Wei Wang, Rui Huang, George Q. Yu, Shiqi Univ Hong Kong Dept Ind & Mfg Syst Engn Hong Kong Peoples R China Southern Univ Sci & Technol Dept Comp Sci & Engn Shenzhen Peoples R China Southern Univ Sci & Technol Res Inst Trustworthy Autonomous Syst Shenzhen Peoples R China Hong Kong Polytechn Univ Dept Ind & Syst Engn Hong Kong Peoples R China

ISBN: (纸本)9798350301298

Video-based gait recognition has achieved impressive results in constrained scenarios. However, visual cameras neglect human 3D structure information, which limits the feasibility of gait recognition in the 3D wild world. Instead of extracting gait features from images, this work explores precise 3D gait features from point clouds and proposes a simple yet efficient 3D gait recognition framework, termed LidarGait. Our proposed approach projects sparse point clouds into depth maps to learn the representations with 3D geometry information, which outperforms existing point-wise and camera-based methods by a significant margin. Due to the lack of point cloud datasets, we build the first large-scale LiDAR-based gait recognition dataset, SUSTech1K, collected by a LiDAR sensor and an RGB camera. The dataset contains 25,239 sequences from 1,050 subjects and covers many variations, including visibility, views, occlusions, clothing, carrying, and scenes. Extensive experiments show that (1) 3D structure information serves as a significant feature for gait recognition. (2) LidarGait outperforms existing point-based and silhouette-based methods by a significant margin, while it also offers stable cross-view results. (3) The LiDAR sensor is superior to the RGB camera for gait recognition in the outdoor environment. The source code and dataset have been made available at https://***.

关键词： Biometrics

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DetCLIPv2: Scalable Open-Vocabulary Object Detection Pre-training via Word-Region Alignment

DetCLIPv2: Scalable Open-Vocabulary Object Detection Pre-tra...

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

作者： Yao, Lewei Han, Jianhua Liang, Xiaodan Xu, Dan Zhang, Wei Li, Zhenguo Xu, Hang Hong Kong Univ Sci & Technol Hong Kong Peoples R China Huawei Noahs Ark Lab Montreal PQ Canada Sun Yat Sen Univ Shenzhen Campus Guangzhou Peoples R China

ISBN: (纸本)9798350301298

This paper presents DetCLIPv2, an efficient and scalable training framework that incorporates large-scale image- text pairs to achieve open-vocabulary object detection (OVD). Unlike previous OVD frameworks that typically rely on a pre-trained vision-language model (e.g., CLIP) or exploit image-text pairs via a pseudo labeling process, DetCLIPv2 directly learns the fine-grained word-region alignment from massive image-text pairs in an end-to-end manner. To accomplish this, we employ a maximum word-region similarity between region proposals and textual words to guide the contrastive objective. To enable the model to gain localization capability while learning broad concepts, DetCLIPv2 is trained with a hybrid supervision from detection, grounding and image-text pair data under a unified data formulation. By jointly training with an alternating scheme and adopting low-resolution input for image-text pairs, DetCLIPv2 exploits image-text pair data efficiently and effectively: DetCLIPv2 utilizes 13x more image-text pairs than DetCLIP with a similar training time and improves performance. With 13M image-text pairs for pre-training, DetCLIPv2 demonstrates superior open-vocabulary detection performance, e.g., DetCLIPv2 with Swin-T backbone achieves 40.4% zero-shot AP on the LVIS benchmark, which outperforms previous works GLIP/GLIPv2/DetCLIP by 14.4/11.4/4.5% AP, respectively, and even beats its fully-supervised counterpart by a large margin.

关键词： and reasoning language vision

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Explicit Boundary Guided Semi-Push-Pull Contrastive Learning for Supervised Anomaly Detection

Explicit Boundary Guided Semi-Push-Pull Contrastive Learning...

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

作者： Yao, Xincheng Li, Ruoqi Zhang, Jing Sun, Jun Zhang, Chongyang Shanghai Jiao Tong Univ Sch Elect Informat & Elect Engn Shanghai Peoples R China Shanghai Jiao Tong Univ AI Inst MoE Key Lab Artificial Intelligence Shanghai Peoples R China Acad Mil Sci Res Inst Syst Engn Beijing Peoples R China

ISBN: (纸本)9798350301298

Most anomaly detection (AD) models are learned using only normal samples in an unsupervised way, which may result in ambiguous decision boundary and insufficient discriminability. In fact, a few anomaly samples are often available in real-world applications, the valuable knowledge of known anomalies should also be effectively exploited. However, utilizing a few known anomalies during training may cause another issue that the model may be biased by those known anomalies and fail to generalize to unseen anomalies. In this paper, we tackle supervised anomaly detection, i.e., we learn AD models using a few available anomalies with the objective to detect both the seen and unseen anomalies. We propose a novel explicit boundary guided semi-push-pull contrastive learning mechanism, which can enhance model's discriminability while mitigating the bias issue. Our approach is based on two core designs: First, we find an explicit and compact separating boundary as the guidance for further feature learning. As the boundary only relies on the normal feature distribution, the bias problem caused by a few known anomalies can be alleviated. Second, a boundary guided semi-push-pull loss is developed to only pull the normal features together while pushing the abnormal features apart from the separating boundary beyond a certain margin region. In this way, our model can form a more explicit and discriminative decision boundary to distinguish known and also unseen anomalies from normal samples more effectively. Code will be available at https://***/xcyao00/BGAD.

关键词： detection recognition: Categorization retrieval

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Difficulty Estimation with Action Scores for computer vision Tasks

Difficulty Estimation with Action Scores for Computer Vision...

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

作者： Arriaga, Octavio Palacio, Sebastian Valdenegro-Toro, Matias Univ Bremen Bremen Germany German Res Ctr Artificial Intelligence Kaiserslautern Germany Univ Groningen Groningen Netherlands

ISBN: (纸本)9798350302493

As more machine learning models are now being applied in real world scenarios it has become crucial to evaluate their difficulties and biases. In this paper we present an unsupervised method for calculating a difficulty score based on the accumulated loss per epoch. Our proposed method does not require any modification to the model, neither any external supervision, and it can be easily applied to a wide range of machine learning tasks. We provide results for the tasks of image classification, image segmentation, and object detection. We compare our score against similar metrics and provide theoretical and empirical evidence of their difference. Furthermore, we show applications of our proposed score for detecting incorrect labels, and test for possible biases.

关键词： Object detection

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