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检索条件"任意字段=2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2024"

共 11889 条记录，以下是11-20 订阅

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Segment Anything in Medical Images with nnUNet

Segment Anything in Medical Images with nnUNet

引用

International Challenge on Segment Anything in Medical Images on Laptop held in conjunction with the ieee/cvf conference on computer vision and pattern recognition, cvpr 2024

作者： Stock, Raphael Kirchhoff, Yannick Rokuss, Maximilian R. Ravindran, Ashis Maier-Hein, Klaus Heidelberg Germany Faculty of Mathematics and Computer Science Heidelberg University Heidelberg Germany HIDSS4Health - Helmholtz Information and Data Science School for Health Karlsruhe Germany HIDSS4Health - Helmholtz Information and Data Science School for Health Heidelberg Germany Pattern Analysis and Learning Group Department of Radiation Oncology Heidelberg University Hospital Heidelberg Germany

ISBN: (纸本)9783031818530

In this paper, we present an enhanced medical image segmentation approach leveraging the nnUNet framework, specifically tailored to integrate bounding box prompts for improved segmentation accuracy in resource-constrained environments. By incorporating these prompts as binary masks in an additional input channel, we enable more precise and context-aware segmentation. Our methodology employs a 2D slice-wise approach optimized for CPU-based inference through just-in-time (JIT) compiled functions, ensuring efficient processing on standard clinical equipment. Our solution demonstrates robust performance, achieving an average Dice Similarity Coefficient (DSC) of 80.98% and a Normalized Surface Dice (NSD) of 83.23% across multiple modalities in the validation set. This indicates its practical applicability and effectiveness in real-world clinical settings, where computational resources may be limited. By focusing on both accuracy and efficiency, our approach makes advanced segmentation technology accessible to a broader range of healthcare providers, facilitating enhanced clinical decision-making and patient care. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Image segmentation

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Channel Propagation Networks for Refreshable vision Transformer

Channel Propagation Networks for Refreshable Vision Transfor...

引用

2025 ieee/cvf Winter conference on Applications of computer vision, WACV 2025

作者： Go, Junhyeong Ryu, Jongbin Ajou University Korea Republic of

ISBN: (纸本)9798331510831

In this paper, we introduce the Channel Propagation method, which aims to increase the channels of the vision Transformer systematically. Skip connections are commonly acknowledged as a propagation approach that improves the stability of the performance in vision Transformers. Nevertheless, it is important to note that these skip connections may give rise to the problem of over-smoothing, wherein similar features are represented in multiple layers. To tackle this matter, our proposed approach for Channel Propagation in vision Transformers retains the present signal information while concurrently propagating location-specific signals in a newly introduced channel dimension. On the other hand, the proposed Channel Propagation method effectively maintains the integrity of identity representation while simultaneously including patch-wise location-specific supervision by introducing a new channel dimension. The inclusion of this approach in vision Transformers mitigates the issue of over-smoothing while also improving the performance of visual recognition tasks. In our experiments, we confirm that the proposed method is effective for various visual recognition tasks. Specifically, our method demonstrates enhanced performance when implemented on vision Transformer models;the classification accuracy is increased considerably for plain and hierarchical architectures on the ImageNet dataset. © 2025 ieee.

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CLIPping Imbalances: A Novel Evaluation Baseline and PEARL Dataset for Pedestrian Attribute recognition

CLIPping Imbalances: A Novel Evaluation Baseline and PEARL D...

引用

2025 ieee/cvf Winter conference on Applications of computer vision, WACV 2025

作者： Thakare, Kamalakar Vijay Lohani, Lalit Nayak, Kamakshya Prasad Dogra, Debi Prosad Choi, Heeseung Jung, Hyungjoo Kim, Ig-Jae Indian Institute of Technology Odisha Bhubaneswar752050 India Artificial Intelligence and Robotics Institute Korea Institute of Science and Technology Seoul02792 Korea Republic of Yonsei-KIST Convergence Research Institute Yonsei University Seoul03722 Korea Republic of

ISBN: (纸本)9798331510831

Pedestrian Attribute recognition (PAR) serves as a fun-damental task in computer vision and is crucial for upgradign security systems. It helps in precisely identifying and characterizing various attributes of pedestrians. However, current PAR datasets have certain issues in representing a wide range of attributes correctly, which makes the ex-isting PAR methods less effective in real-world scenarios. Addressing this limitation, this paper introduces PEARL, a comprehensive dataset comprising of diverse pedestrian images annotated with 146 attributes. These samples have been sourced from surveillance videos across twelve coun-tries. This paper also formulates an image-based PAR using language-image fusion strategy and utilizes CLIP as a new evaluation baseline. Specifically, we leverage textual infor-mation by transforming sets of attributes into meaningful sentences. Addressing the inherent data imbalance in PAR, we provide three types of prompt settings to optimize the training of the CLIP model. Our evaluation encompasses a thorough assessment of the proposed baseline model across various datasets, including PEARL dataset as well as estab-lished PAR benchmarks such as PA100K, RAP, and PETA. © 2025 ieee.

关键词： Contrastive Learning

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TRH2TQA: Table recognition with Hierarchical Relationships to Table Question-Answering on Business Table Images

TRH2TQA: Table Recognition with Hierarchical Relationships t...

引用

2025 ieee/cvf Winter conference on Applications of computer vision, WACV 2025

作者： Jirachanchaisiri, Pongsakorn Ly, Nam Tuan Takasu, Atsuhiro National Institute of Informatics Tokyo Japan Kanagawa Japan Tokyo University of Agriculture and Technology Tokyo Japan

ISBN: (纸本)9798331510831

Despite advancements in visual question answering, challenges persist with documents like financial reports, often structured in complicated tabular structures with complex numerical computations. An alternative approach, the pipeline-driven methodology, includes table recognition (TR) and table question-answering (TQA). Recent advancements in TR support this approach with better accuracy and interpretability. However, real-world tables usually represent hierarchical tables. They pose additional challenges due to merged cells and indents, necessitating a specific approach for hierarchical relationship extraction. In this paper, we propose TRH2TQA (Table recognition with Hierarchical Relationships to Table Question-Answering) for business table images. It consists of three modules on table images with question-answer pairs. First, the TR module extracts structure and textual content from table images into HTML format. Second, post-structure extraction is applied to identify header and hierarchical relationships using predicted column span and bounding box. Finally, this information is combined with natural language questions in the TQA module to generate the answer through the decoder. In extensive experiments, TRH2TQA outperforms in questionanswering performance on the VQAonBD 2023 dataset. © 2025 ieee.

关键词： Character recognition

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Stratified Domain Adaptation: A Progressive Self-Training Approach for Scene Text recognition

Stratified Domain Adaptation: A Progressive Self-Training Ap...

引用

2025 ieee/cvf Winter conference on Applications of computer vision, WACV 2025

作者： Le, Kha Nhat Nguyen, Hoang-Tuan Tran, Hung Tien Ngo, Thanh Duc University of Information Technology VNU-HCM Viet Nam Vietnam National University Ho Chi Minh City Viet Nam

ISBN: (纸本)9798331510831

Unsupervised domain adaptation (UDA) has become increasingly prevalent in scene text recognition (STR), especially where training and testing data reside in different domains. The efficacy of existing UDA approaches tends to degrade when there is a large gap between the source and target domains. To deal with this problem, gradually shifting or progressively learning to shift from domain to domain is the key issue. In this paper, we introduce the Stratified Domain Adaptation (StrDA) approach, which examines the gradual escalation of the domain gap for the learning process. The objective is to partition the target data into subsets so that the progressively self-trained model can adapt to gradual changes. We stratify the target data by evaluating the proximity of each data sample to both the source and target domains. We propose a novel method for employing domain discriminators to estimate the out-of-distribution and domain discriminative levels of data samples. Extensive experiments on benchmark scene-text datasets show that our approach significantly improves the performance of baseline (source-trained) STR models. The source code is available at https://***/KhaLee2307/StrDA. © 2025 ieee.

关键词： domain adaptation scene text recognition self-training

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Semantically Conditioned Prompts for Visual recognition Under Missing Modality Scenarios

Semantically Conditioned Prompts for Visual Recognition Unde...

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2025 ieee/cvf Winter conference on Applications of computer vision, WACV 2025

作者： Pipoli, Vittorio Bolelli, Federico Sarto, Sara Cornia, Marcella Baraldi, Lorenzo Grana, Costantino Cucchiara, Rita Ficarra, Elisa University of Modena and Reggio Emilia Italy University of Pisa Italy

ISBN: (纸本)9798331510831

This paper tackles the domain of multimodal prompting for visual recognition, specifically when dealing with missing modalities through multimodal Transformers. It presents two main contributions: (i) we introduce a novel prompt learning module which is designed to produce sample-specific prompts and (ii) we show that modalityagnostic prompts can effectively adjust to diverse missing modality scenarios. Our model, termed SCP, exploits the semantic representation of available modalities to query a learnable memory bank, which allows the generation of prompts based on the semantics of the input. Notably, SCP distinguishes itself from existing methodologies for its capacity of self-adjusting to both the missing modality scenario and the semantic context of the input, without prior knowledge about the specific missing modality and the number of modalities. Through extensive experiments, we show the effectiveness of the proposed prompt learning framework and demonstrate enhanced performance and robustness across a spectrum of missing modality cases. Our source code is available at https://***/vittoriopipoli/SCP_WACV2025. © 2025 ieee.

关键词： missing modalities multimodal learning prompt learning transformer

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Classification of Colorectal Cancer Tissues Using Stacking Ensemble Learning 7th

Classification of Colorectal Cancer Tissues Using Stacking E...

引用

7th International conference on Communication, Device and Networking, ICCDN 2024

作者： Das, Abhrodeep Hazra, Animesh Department of Computer Science and Engineering Jalpaiguri Government Engineering College West Bengal Jalpaiguri735102 India

ISBN: (纸本)9789819764648

Advancement in digital pathology has enabled deep learning-based computer vision techniques for automated diagnosis and prognosis of diseases. The essentiality of early detection and prognosis of any cancer category lies in the fact that it can speed up the subsequent medical treatment procedures of patients. About 10% of all cancer cases worldwide are related to colorectal cancer (CRC), and it is also the third most common category of cancer (Egeblad et al. in Dev Cell 18:884–901, 2010). So, it is clinically important to classify and make an objective evaluation of colorectal cancer histological images. The classification performance of current methodologies primarily relies on the use of various combinations of texture-based features and classifiers or transfer learning to classify different organisational kinds. As a result of the diversity of tissue types and characteristics present in histological images, classification is still a challenging task. In this study, we have put forth a novel and effective stacking (Wolpert in Neural Netw 5:241–59, 1992) ensemble (Zhang and Yunqian (eds) Ensemble machine learning: methods and applications. Springer Science;Business Media, 2012) technique for classification on the histopathological image analysis benchmark dataset Kather-5 K (Kather et al. in Sci Rep 6:27988, 2016). The ensemble consists of two cutting-edge deep learning architectures, ResNet18 (He et al. in Proceedings of the ieee conference on computer vision and pattern recognition 2016, pp 770–778) and EfficientNetB0 (Tan and Le Efficientnet: rethinking model scaling for convolutional neural networks. International conference on machine learning 2019 May 24. PMLR, pp 6105–6114), acting as weak learners, and an ANN acting as the meta-learner. The proposed approach obtained a remarkable accuracy of 97.20% in CRC classification on the said dataset. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Colorectal cancer Convolutional neural network Ensemble learning Residual network Stacking

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Non-autoregressive Sequence-to-Sequence vision-Language Models

Non-autoregressive Sequence-to-Sequence Vision-Language Mode...

引用

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

作者： Shi, Kunyu Dong, Qi Goncalves, Luis Tu, Zhuowen Soatto, Stefano AWS AI Labs Seattle WA 98101 USA

ISBN: (纸本)9798350353006

Sequence-to-sequence vision-language models are showing promise, but their applicability is limited by their inference latency due to their autoregressive way of generating predictions. We propose a parallel decoding sequence-to-sequence vision-language model, trained with a Query-CTC loss, that marginalizes over multiple inference paths in the decoder. This allows us to model the joint distribution of tokens, rather than restricting to conditional distribution as in an autoregressive model. The resulting model, NARVL, achieves performance on-par with its state-of-the-art autoregressive counterpart, but is faster at inference time, reducing from the linear complexity associated with the sequential generation of tokens to a paradigm of constant time joint inference.

关键词： CTC Non-autoregressive vision language models

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Efficient vision-Language Pre-training by Cluster Masking

Efficient Vision-Language Pre-training by Cluster Masking

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

作者： Wei, Zihao Pan, Zixuan Owens, Andrew Univ Michigan Ann Arbor MI 48109 USA

ISBN: (纸本)9798350353006

We propose a simple strategy for masking image patches during visual-language contrastive learning that improves the quality of the learned representations and the training speed. During each iteration of training, we randomly mask clusters of visually similar image patches, as measured by their raw pixel intensities. This provides an extra learning signal, beyond the contrastive training itself, since it forces a model to predict words for masked visual structures solely from context. It also speeds up training by reducing the amount of data used in each image. We evaluate the effectiveness of our model by pre-training on a number of benchmarks, finding that it outperforms other masking strategies, such as FLIP, on the quality of the learned representation.

关键词： Contrastive Learning

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An Investigation into the Impact of AI-Powered Image Enhancement on Forensic Facial recognition

An Investigation into the Impact of AI-Powered Image Enhance...

引用

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

作者： Norman, Justin Farid, Hany Univ Calif Berkeley Berkeley CA 94720 USA

ISBN: (纸本)9798350365474

Advances in machine learning and computer vision have led to significant improvements in automated facial recognition. Many real-world forensic settings, however, are confronted with challenging low-quality and low-resolution images that often confound even state-of-the art facial recognition. We investigate if and when advances in neural-based image enhancement and restoration can be used to restore degraded images while preserving facial identity for use in forensic facial recognition.

关键词： Forensic Facial recognition Image Enhancement

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