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检索条件"任意字段=7th Chinese Conference on Pattern Recognition and Computer Vision"

共 2177 条记录，以下是11-20 订阅

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7th chinese conference on pattern recognition and computer vision, PRCV 2024

7th Chinese Conference on Pattern Recognition and Computer V...

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7th chinese conference on pattern recognition and computer vision, PRCV 2024

ISBN: (纸本)9789819786916

the proceedings contain 81 papers. the special focus in this conference is on pattern recognition and computer vision. the topics include: FUF-TransUNet: A Transformer-Based U-Net with Fully Utilize of Features for Liver and Liver-Tumor Segmentation in CT Images;Dual-View Dual-Boundary Dual U-Nets for Multiscale Segmentation of Oral CBCT Images;a Novel Diffusion Model with Wavelet Transform for Optic Disc and Cup Segmentation in Fundus Images;STCTb: A Spatio-Temporal Collaborative Transformer Block for Brain Diseases Classification Using fMRI Time Series;a Generalized Contrast-Adjustment Guided Growth Method for Medical Image Segmentation;MDNet: Morphology-Driven Weakly Supervised Polyp Detection;MMR-Sleep: A Multi-Channel and Multi-Receptive Field Sleep Stage recognition Model;CPNet: Cross Prototype Network for Few-Shot Medical Image Segmentation;SBC-UNet: A Network Based on Improved Hourglass Attention Mechanism and U-Net for Medical Image Segmentation;Bridge the Gap of Semantic Context: A Boundary-Guided Context Fusion UNet for Medical Image Segmentation;bilinear Fine-grained Classification of Ultrasound Images Integrated with Interpretable Radiomics;GCNet: Global Context-Guided Uncertainty Boundary for Polyp Segmentation;Comprehensive Transformer Integration Network (CTIN): Advancing Endoscopic Disease Segmentation with Hybrid Transformer Architecture;IPM: An Intelligent Component for 3D Brain Tumor Segmentation Integrating Semantic Extractor and Pixel Refiner;edge-Net: A Self-supervised Medical Image Segmentation Model Based on Edge Attention;fundus Image Disease Diagnosis and Quality Assessment Based on Dual-Task Collaborative Optimization;multi-modality Correlation Learning Network for Pediatric Ventricular Septal Defects Identification;MFIS-Net: A Deep Learning Framework for Left Atrial Segmentation;semi-supervised Gland Segmentation via Label Purification and Reliable Pixel Learning;DFANet: A Dual-Stream Deep Feature Aware Network for Multi-focus Image Fusi

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7th chinese conference on pattern recognition and computer vision, PRCV 2024

7th Chinese Conference on Pattern Recognition and Computer V...

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7th chinese conference on pattern recognition and computer vision, PRCV 2024

ISBN: (纸本)9789819784899

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7th chinese conference on pattern recognition and computer vision, PRCV 2024

7th Chinese Conference on Pattern Recognition and Computer V...

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7th chinese conference on pattern recognition and computer vision, PRCV 2024

ISBN: (纸本)9789819784981

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7th chinese conference on pattern recognition and computer vision, PRCV 2024

7th Chinese Conference on Pattern Recognition and Computer V...

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7th chinese conference on pattern recognition and computer vision, PRCV 2024

ISBN: (纸本)9789819786848

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7th chinese conference on pattern recognition and computer vision, PRCV 2024

7th Chinese Conference on Pattern Recognition and Computer V...

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7th chinese conference on pattern recognition and computer vision, PRCV 2024

ISBN: (纸本)9789819785100

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Improving Video Representation of vision-Language Model with Decoupled Explicit Temporal Modeling 7th

Improving Video Representation of Vision-Language Model with...

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7th chinese conference on pattern recognition and computer vision

作者： Liu, Yuxi Zheng, Wenyu Chen, Sihong Zheng, Xinming Univ Sci & Technol China Hefei Anhui Peoples R China Tencent Shenzhen Guangdong Peoples R China

ISBN: (纸本)9789819785100;9789819785117

vision-Language Pre-trained (VLP) models have shown significant ability in many video tasks. For action recognition, recent studies predominantly use meticulously designed prompt tokens or positional encodings to adapt VLP models to video domains, consequently leading to a reliance on designing and learning processes. Moreover, in mainstream fine-tuning settings, models are guided by downstream tasks, which is a coarse-grained objective toward temporal modeling. To address these issues we propose an Explicit Temporal Modeling (ETM) method that mainly consists of two key designs and is decoupled from the image model. To add temporal supervision, we focus on frame-sequential order and design a temporal-related task in a contrastive manner. To reduce dependence on the quality of design and learning when modeling temporality, we propose a module with temporality-aware computation approaches and make it compatible with the newly added task. Extensive experiments are conducted on real-world datasets, demonstrating that our proposed ETM can improve VLP models' performance on action recognition tasks. Besides, our model also performs generalization ability in few/zero-shot tasks. Code and supplementary are available at https://***/lyxwest/ETM.

关键词： vision-language pre-trained Video understanding Action recognition

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Spectral Channel-Weighting CAT for Hyperspectral Image Classification 7th

Spectral Channel-Weighting CAT for Hyperspectral Image Class...

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7th chinese conference on pattern recognition and computer vision

作者： Qi, Yujuan Guo, Yuxuan Liu, Baodi Wang, Yanjiang China Univ Petr East China Coll Control Sci & Engn Qingdao 266580 Peoples R China

ISBN: (纸本)9789819784929;9789819784936

In recent Hyperspectral image (HSI) classification research, vision Transformer (ViT) has gained significant attention. However, ViT often struggles with the computational complexity of handling HSI patches and may not effectively emphasize crucial spectral bands. To tackle these challenges, this paper developes a novel HSI classification network called Spectral Channel-weighting Cross Attention in vision Transformer (SCCAT). First, we integrate Cross Attention in vision Transformer (CAT) into HSI classification, reducing computational requirements and achieving efficient classification. Second, we construct a Spectral Channel Weighting (SCW) module to address the network's limited attention to spectral information. Finally, through extensive experiments on Indian Pines and Salinas datasets, we validate the superior classification performance of SCCAT in terms of accuracy.

关键词： vision Transformer Cross Attention in vision Transformer Channel Weighting hyperspectral image classification

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Patch Attacks on vision Transformer via Skip Attention Gradients 7th

Patch Attacks on Vision Transformer via Skip Attention Gradi...

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7th chinese conference on pattern recognition and computer vision

作者： Deng, Haoyu Fang, Yanmei Huang, Fangjun Sun Yat Sen Univ Sch Cyber Sci & Technol Shenzhen Campus Shenzhen Peoples R China Guangdong Prov Key Lab Informat Secur Technol Guangzhou 510006 Peoples R China

ISBN: (纸本)9789819786848;9789819786855

vision Transformers (ViTs) have demonstrated exceptional performance across various computer vision tasks. ViTs utilize the attention mechanism for feature extraction, thereby enhancing global information capture compared to convolutional neural networks. Currently, the adversarial attack methods designed for ViTs focus on creating adversarial samples using the degree of attention between tokens, while neglecting the impact of ViT's nonlinear factors. In this paper, we propose to Skip Attention Gradient (SAG) to avoid the nonlinear effects of attention by leveraging the linear nature of ViTs to generate adversarial examples. We demonstrate that the adversarial patch selected by SAG can promote a more linear propagation of adversarial effects, thereby enhancing the attack's effectiveness. Our method achieves a robust accuracy on DeiT-B that is 7% lower compared to the Patch-Fool method.

关键词： White-box attacks Patch attacks vision Transformer

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Dual-Stream Network of vision Mamba and CNN with Auto-Scaling for Remote Sensing Image Segmentation 7th

Dual-Stream Network of Vision Mamba and CNN with Auto-Scalin...

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7th chinese conference on pattern recognition and computer vision

作者： Song, Shitao Liu, Ye Su, Jintao Nanjing Univ Posts & Telecommun Sch Automat & Artificial Intelligence Nanjing 210003 Peoples R China

ISBN: (纸本)9789819785049;9789819785056

vision Mamba (VMamba) has recently attracted great research attention due to its ability to obtain a global receptive field with linear computational complexity. However, similar to vision Transformer (ViT), due to its mechanism of dividing patches, it also faces the issue of insufficient description ability of local details. To address this issue, we design in this paper a dual-stream network that combines VMamba and CNN, aiming to enable the network to possess both the global receptive field of VMamba and the local detail description capability of CNN. Both of the two characteristics are crucial for remote sensing image semantic segmentation. the two streams are supervised and trained through independent loss functions. On the other hand, to enable sufficient information exchange between the two branches, we introduce an auto-scaling fusion module aiming at bridging the semantic gap between VMamba and CNN. Experiments demonstrate that the method proposed in this paper outperforms state-of-the-art methods on multiple remote sensing semantic segmentation datasets.

关键词： vision Mamba CNN Auto-scaling Semantic segmentation Remote sensing

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Evaluating Attribute Comprehension in Large vision-Language Models 7th

Evaluating Attribute Comprehension in Large Vision-Language ...

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7th chinese conference on pattern recognition and computer vision

作者： Zhang, Haiwen Yang, Zixi Liu, Yuanzhi Wang, Xinran He, Zheqi Liang, Kongming Ma, Zhanyu Beijing Univ Posts & Telecommun Beijing 100876 Peoples R China Beijing Acad Artificial Intelligence Beijing 100084 Peoples R China

ISBN: (纸本)9789819786190;9789819786206

Currently, large vision-language models have gained promising progress on many downstream tasks. However, they still suffer many challenges in fine-grained visual understanding tasks, such as object attribute comprehension. Besides, there have been growing efforts on the evaluations of large vision-language models, but lack of in-depth study of attribute comprehension and the visual language fine-tuning process. In this paper, we propose to evaluate the attribute comprehension ability of large vision-language models from two perspectives: attribute recognition and attribute hierarchy understanding. We evaluate three vision-language interactions, including visual question answering, image-text matching, and image-text cosine similarity. Furthermore, we explore the factors affecting attribute comprehension during fine-tuning. through a series of quantitative and qualitative experiments, we introduce three main findings: (1) Large vision-language models possess good attribute recognition ability, but their hierarchical understanding ability is relatively limited. (2) Compared to ITC, ITM exhibits superior capability in capturing finer details, making it more suitable for attribute understanding tasks. (3) the attribute information in the captions used for fine-tuning plays a crucial role in attribute understanding. We hope this work can help guide future progress in fine-grained visual understanding of large vision-language models. the code will be available at Attribute-Comprehension-of-VLMs.

关键词： Large vision-Language Models Attribute recognition Hierarchical Understanding

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