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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21-30 订阅

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Pseudo-Prompt Generating in Pre-trained vision-Language Models for Multi-label Medical Image Classification 7th

Pseudo-Prompt Generating in Pre-trained Vision-Language Mode...

引用

7th chinese conference on pattern recognition and computer vision

作者： Ye, Yaoqin Zhang, Junjie Shi, Hongwei Sichuan Univ Coll Comp Sci Chengdu Peoples R China

ISBN: (纸本)9789819784950;9789819784967

the task of medical image recognition is notably complicated by the presence of varied and multiple pathological indications, presenting a unique challenge in multi-label classification with unseen labels. this complexity underlines the need for computer-aided diagnosis methods employing multi-label zero-shot learning. Recent advancements in pre-trained vision-language models (VLMs) have showcased notable zero-shot classification abilities on medical images. However, these methods have limitations on leveraging extensive pre-trained knowledge from broader datasets, and often depend on manual prompt construction by expert radiologists. By automating the process of prompt tuning, prompt learning techniques have emerged as an efficient way to adapt VLMs to downstream tasks. Yet, existing CoOp-based strategies fall short in performing class-specific prompts on unseen categories, limiting generalizability in fine-grained scenarios. To overcome these constraints, we introduce a novel prompt generation approach inspirited by text generation in natural language processing (NLP). Our method, named Pseudo-Prompt Generating (PsPG), capitalizes on the priori knowledge of multi-modal features. Featuring a RNN-based decoder, PsPG autoregressively generates class-tailored embedding vectors, i.e., pseudo-prompts. Comparative evaluations on various multi-label chest radiograph datasets affirm the superiority of our approach against leading medical vision-language and multi-label prompt learning methods. the source code is available at https://***/fallingnight/PsPG.

关键词： Prompt Learning Medical Image recognition Multi-label Classification vision-Language Models

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ViPro-BEV: Few-Shot Visual Prompting for Bird's-Eye-View Perception 7th

ViPro-BEV: Few-Shot Visual Prompting for Bird's-Eye-View Per...

引用

7th chinese conference on pattern recognition and computer vision

作者： Yuan, Guorong Huang, Huaibo Fan, Qihang Univ Chinese Acad Sci Sch Artificial Intelligence Beijing Peoples R China Chinese Acad Sci Inst Automat New Lab Pattern Recognit NLPR Beijing Peoples R China

ISBN: (纸本)9789819787913;9789819787920

In the dynamically progressing field of computer vision, the task of Bird's-Eye-View (BEV), which offers an encompassing top-down perspective by integrating data from various sensors, has become indispensable, particularly for autonomous driving systems. Adapting BEV models to downstream datasets presents a significant challenge due to the scarcity of effective fine-tuning approaches. To surmount these challenges, we propose Few-Shot Visual Prompt (ViPro), a modular Plug-and-Play methodology that facilitates the transfer of models across datasets with minimal data usage, marking a pioneering approach in the BEV sphere. Our methodology employs a two-phase pixel-wise prompting strategy: initially, a subset of source dataset is simpled to train prototype visual prompts that capture the general features of images. Subsequently, the dataset is categorized into clusters to distinguish different scenes, enhancing scene-specific adaptability. this approach is further augmented by token-wise visual prompts within the Cross-View Transformer, bolstering the model's performance and flexibility while significantly reducing susceptibility to overfitting specific environmental factors.

关键词： Autonomous driving Bird's-eye-view Few-shot adaptation

来源：评论

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Fake-GPT: Detecting Fake Image via Large Language Model 7th

Fake-GPT: Detecting Fake Image via Large Language Model

引用

7th chinese conference on pattern recognition and computer vision

作者： Fan, Yuming Yang, Dongming Zhang, Jiguang Yan, Bang Zou, Yuexian China Telecom Cloud Technol Co Ltd Beijing Peoples R China Chinese Acad Sci Inst Automat Beijing Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing Peoples R China Peking Univ Beijing Peoples R China

ISBN: (纸本)9789819786848;9789819786855

With the development of Artificial Intelligence Generated Content (AIGC), fake image detection has become increasingly challenging. Also leveraging the advanced capabilities of large language models (LLMs) in sequence prediction, we propose a novel perspective on fake image detection by fine-tuning pure LLMs. We introduce Fake-GPT, a LLM with 7 billion parameters which can differentiate between real and fake images. Unlike conventional image processing models, our approach directly process RGB pixel values without relying on any position embedding and visual-language feature alignment, thereby reducing model complexity and processing steps. Our research demonstrates the effective application of LLMs in detecting fake images, thereby expanding their application in non-textual domains. Extensive experiments conducted on various deepfake datasets show that Fake-GPT achieves competitive results compared with conventional image processing models, underscoring its potential as a new paradigm in the realm of image authentication.

关键词： Visual languages

来源：评论

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Adapting vision-Language Models to Open Classes via Test-Time Prompt Tuning 7th

Adapting Vision-Language Models to Open Classes via Test-Tim...

引用

7th chinese conference on pattern recognition and computer vision

作者： Gao, Zhengqing Ao, Xiang Zhang, Xu-Yao Liu, Cheng-Lin Chinese Acad Sci Inst Automat MAIS Beijing Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing Peoples R China

ISBN: (纸本)9789819786190;9789819786206

Adapting pre-trained models to open classes is a challenging problem in machine learning. vision-language models fully explore the knowledge of text modality, demonstrating strong zero-shot recognition performance, which is naturally suited for various open-set problems. More recently, some research focuses on fine-tuning such models to downstream tasks. Prompt tuning methods achieved huge improvements by learning context vectors on few-shot data. However, through the evaluation under open-set adaptation setting with the test data including new classes, we find that there exists a dilemma that learned prompts have worse generalization abilities than hand-crafted prompts. In this paper, we consider combining the advantages of both and come up with a test-time prompt tuning approach, which leverages the maximum concept matching (MCM) scores as dynamic weights to generate an input-conditioned prompt for each image during test. through extensive experiments on 11 different datasets, we show that our proposed method outperforms all comparison methods on average considering both base and new classes. the code is available at https://***/gaozhengqing/TTPT.

关键词： vision-language models Test-time adaptation Prompt tuning

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A³R: vision Language Pre-training by Attentive Alignment and Attentive Reconstruction 7th

A<SUP>3</SUP>R: Vision Language Pre-training by Attentive Al...

引用

7th chinese conference on pattern recognition and computer vision

作者： Hu, Yusong Gao, Yuting Xu, Zihan Li, Ke Liu, Xialei Nankai Univ VCIP CS Tianjin Peoples R China Tencent Youtu Lab Shanghai Peoples R China

ISBN: (纸本)9789819786190;9789819786206

Recently, vision-language models such as ALBEF/CoCa have achieved significant success in various downstream tasks. However, due to the inherent differences between visual and language modalities, they often encounter three challenges: semantics mismatch, fine-grained alignment missing, and trivial masking. To address these issues, we propose to use intrinsic attention scores within the model to guide the cross-modal interaction through attentive alignment and attentive reconstruction. these approaches help the model to focus on the most important information and avoid inefficient processes. Extensive experiments on several downstream tasks demonstrate the effectiveness of the proposed A(3)R. Specifically, on the zero-shot Flickr30K retrieval task, A(3)R brings a 1.2%/1.8% improvement in top-1 hit accuracy of image-to-text/text-to-image retrieval, using only 76% of the training time and 68% memory compared to the baseline.

关键词： vision-language pre-training Cross-modal learning

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More Like Real World Game Challenge for Partially Observable Multi-agent Cooperation 7th

More Like Real World Game Challenge for Partially Observable...

引用

7th chinese conference on pattern recognition and computer vision

作者： Feng, Xueou Yao, Meng Shen, Shengqi Yin, Qiyue Yang, Jun Chinese Acad Sci Inst Automat Beijing 100190 Peoples R China Jianghuai Adv Technol Ctr Hefei 230000 Peoples R China Tsinghua Univ Dept Automat Beijing 100084 Peoples R China

ISBN: (纸本)9789819785049;9789819785056

Partially observable multi-agent cooperation (POMAC) is a popular task in multi-agent systems, where recognized environments play a vital role for algorithms development and testing like the StarCraft Multi-Agent Challenge. However, POMAC in real world often faces more complex situations beyond the simulation scope of current environments, such as asynchronous cooperation, which largely limits the development of multi-agent cooperation algorithms. To cope with this gap, we propose WarGame Challenge (WGC), which provides four sub-environments to reflect reality-inspired characteristics in POMAC, i.e., cooperation with asynchronous actions, strongly stochastic environments, changeable agents, and asymmetric opponents. Along with the benchmark, we embed Pymarl package and provide baseline multi-agent reinforcement learning algorithms for researchers' use. the overall codes and projects will be released after the paper review process.

关键词： Multi-agent learning Benchmark environment Deep reinforcement learning

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BEVDot: Enhancing Environmental Perception for Autonomous Driving with a Deformable Depth Mechanism 7th

BEVDot: Enhancing Environmental Perception for Autonomous Dr...

引用

7th chinese conference on pattern recognition and computer vision

作者： Yang, Chunmeng Lai, Zeyu Lu, Gaofeng Kong, Bin Univ Sci & Technol China Hefei 230026 Peoples R China Chinese Acad Sci Hefei Inst Phys Sci Hefei 230031 Peoples R China Anhui Engn Res Ctr Intelligent Driving Technol & Hefei 230031 Peoples R China

ISBN: (纸本)9789819787913;9789819787920

In the field of autonomous driving, environmental perception is crucial for driving safety. Addressing the limitations of existing visual perception methods in complex scenarios, this study proposes a deformable depth visual perception framework based on a multi-camera system. the framework processes multi-camera data through a feature extraction network to generate and fuse multi-scale features. And a deformable depth prediction mechanism incorporating self-vehicle temporal difference features is introduced to enhance the accuracy of the model in depth prediction. Experimental results show that on the NuScenes dataset, our method achieves a detection accuracy (mAP) of 0.508 using only 5 random cameras out of 6, surpassing existing technologies such as Lift-Splat (0.446), RC-BEVFusion (0.476), and SOGDet-SE (0.474). Future research will focus on improving the prediction accuracy of distant vehicles to further enhance the performance of the model.

关键词： Automated Driving Visual Perception Vehicle Detection

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ODC-SA Net: Orthogonal Direction Enhancement and Scale Aware Network for Polyp Segmentation 7th

ODC-SA Net: Orthogonal Direction Enhancement and Scale Aware...

引用

7th chinese conference on pattern recognition and computer vision

作者： Xu, Chenhao Zhang, Yudian Xu, Kaiye Zhu, Haijiang Beijing Univ Chem Technol CIST Beijing 100029 Peoples R China

ISBN: (纸本)9789819784950;9789819784967

Accurate polyp segmentation is crucial for the early detection of colorectal cancer. However, existing polyp detection methods sometimes ignore multi-directional features and the drastic scale changes of concealed targets. To address these challenges, we design an Orthogonal Direction Enhancement and Scale Aware Network (ODC-SA Net) for polyp segmentation. the Orthogonal Direction Convolutional (ODC) block can extract multi-directional features using transposed rectangular convolution kernels through forming sets of orthogonal feature vector basis, which solves the issue of random feature direction changes. Additionally, the Multi-scale Fusion Attention (MSFA) mechanism is proposed to emphasize scale changes in both spatial and channel dimensions, enhancing the segmentation accuracy for polyps of varying sizes. Extraction with Re-attention (ERA) module is used to re-combine effective features, and Shallow Reverse Attention (SRA) mechanism is used to enhance polyp edge with low level information. A large number of experiments conducted on public datasets have demonstrated that the performance of this model is superior to state-of-the-art methods.

关键词： Polyp segmentation Colonoscopy Medical image processing computer vision

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Multi-Modal Attention Based on 2D Structured Sequence for Table recognition 7th

Multi-Modal Attention Based on 2D Structured Sequence for Ta...

引用

7th chinese conference on pattern recognition and computer vision

作者： Zhang, Yiming Zhang, Yaping Xiang, Lu Zhou, Yu Chinese Acad Sci Inst Automat State Key Lab Multimodal Artificial Intelligence Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100049 Peoples R China Zhongke Fanyu Technol Co Ltd Fanyu AI Lab Beijing 100190 Peoples R China

ISBN: (纸本)9789819785100;9789819785117

Table Structure recognition (TSR) aims to extract two-part information from a table image: a 2D structured language sequence and a bounding box sequence. Image-to-text (i2t) methods have received more attention among recent TSR methods. However, recent i2t methods use (1) a dual-decoder framework for the two-part output, which is complex and hard to design, and (2) the vanilla attention layer for predicting 2D structured sequences, which is inappropriate because the vanilla attention is designed for 1D sequences. To address these problems, (1) we are the first to propose a novel encoder-decoder framework based on i2t methods, which discards the old dual-decoder framework, making it more uniform and easier to design. Our encoder-decoder architecture is composed of three modules: Multi-Modal Encoder, Multi-Modal Mid-Block, and Multi-Modal Decoder. (2) We also propose a novel 2D attention layer, which explicitly models the 2D features of the structured language. Finally, we test our method on public datasets and achieve significant improvements in visual prediction and comparable results in text prediction.

关键词： Table structure recognition 2D Attention Multi-modal table learning

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Species-Aware Guidance for Animal Action recognition with vision-Language Knowledge 7th

Species-Aware Guidance for Animal Action Recognition with Vi...

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7th chinese conference on pattern recognition and computer vision

作者： Zhai, Zhen Zhang, Hailun Zhao, Qijun Fu, Keren Sichuan Univ Coll Comp Sci Chengdu Peoples R China

ISBN: (纸本)9789819785100;9789819785117

Species diversity is one of the major differences between animal action recognition and human action recognition, resulting in a series of challenges, e.g., action manifestation diversity, concurrent actions, and long-tailed distribution in datasets. As the same action can be manifested significantly differently among animal species due to their physiological differences, it is crucial for models to distinctively learn various visual content under the same label with species-aware perspectives. However, previous works mainly applied single-species recognition methods to animal datasets, without considering species diversity to address animal action recognition. To fill this gap, we propose a novel animal action recognition approach with specific species guidance by exploring pre-trained vision-language knowledge, namely Species-Aware Guidance (SAG). Firstly, we add word-level species semantics to visual embeddings as guidance, leading the model to focus on relevant regions of target animals in subsequent visual understanding. then, we apply spatiotemporal modeling in both global and local granularity via a two-branch module to obtain a cross-modal video representation. Finally, sentence-level species-aware semantics is fused with action labels as an overall query, guiding the video representation to output the final action label via the decoder. On two widely used public benchmarks of animal action recognition, for both single-label and multi-label scenarios, SAG archives state-of-the-art performance, e.g., Animal Kingdom (up arrow 5.0%), Mammalnet (up arrow 27.0%) compared to existing methods, especially well-alleviating the problem of long-tailed distributions, demonstrating the effectiveness of species guidance under limited data for training.

关键词： Animal Action recognition vision-Language Models Multi-label Classification

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