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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31-40 订阅

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Feature Refinement and Calibration for Continual Visual Search 7th

Feature Refinement and Calibration for Continual Visual Sear...

引用

7th chinese conference on pattern recognition and computer vision

作者： Su, Qinghang Chen, Xiaohua Gu, Jingzi Li, Bo Chinese Acad Sci Inst Informat Engn Beijing Peoples R China Univ Chinese Acad Sci Sch Cyber Secur Beijing Peoples R China Key Lab Cyberspace Secur Def Beijing Peoples R China

ISBN: (纸本)9789819786916;9789819786923

Continual visual search has attracted increasing attention due to its practicality. It aims to continuously extract embeddings for new-emerging gallery data while keeping previously generated embeddings unchanged. the primary challenge lies in preserving embedding compatibility between ongoing and past data. Existing methods mainly concentrate on ensuring backward embedding consistency. However, they often overlook the issue of semantic drift, which arises from overlapping semantics between old and new-class data, thus hindering further progress. To address this problem, we propose a novel feature refinement and calibration module for continual visual search. Specifically, we introduce a feature refinement module aimed at enhancing features to be more discriminative concerning class distinctions. Subsequently, we employ a feature calibration module to further eliminate overlapping semantics, thereby avoiding the confusion between previously and currently learned knowledge. Extensive experiments demonstrate the effectiveness of both the feature refinement module and the feature calibration module. Our approach consistently outperforms existing state-of-the-art methods.

关键词： Image retrieval Continual learning Feature refinement and calibration

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Variational Capsules for Image Analysis and Synthesis 7th

Variational Capsules for Image Analysis and Synthesis

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7th chinese conference on pattern recognition and computer vision

作者： Zhang, Yuguang Huang, Huaibo Univ Chinese Acad Sci Sch Artificial Intelligence Beijing Peoples R China Chinese Acad Sci Inst Automat MAIS & NLPR Beijing Peoples R China China Elect Standardizat Insitute Beijing Peoples R China MIIT Key Lab New Generat Informat Secur & Privacy Beijing Peoples R China

ISBN: (纸本)9789819786916;9789819786923

A capsule is a group of neurons whose activity vector models different properties of the same entity. this paper extends the capsule to a generative version, named variational capsules (VCs). Each VC produces a latent variable for a specific entity, making it possible to integrate image analysis and image synthesis into a unified framework. Variational capsules model an image as a composition of entities in a probabilistic model. Different capsules divergence with a specific prior distribution represents the presence of different entities, which can be applied in image analysis tasks such as classification. In addition, variational capsules encode multiple entities in a semantically-disentangling way. Diverse instantiations of capsules are related to various properties of the same entity, making it easy to generate diverse samples with fine-grained semantic attributes. Extensive experiments demonstrate that deep networks designed with variational capsules can not only achieve promising performance on image analysis tasks (including image classification and attribute prediction) but can also improve the diversity and controllability of image synthesis.

关键词： Image analysis and synthesis Condtional image generation Capsules VAE

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Multi-modal Knowledge-Enhanced Fine-Grained Image Classification 7th

Multi-modal Knowledge-Enhanced Fine-Grained Image Classifica...

引用

7th chinese conference on pattern recognition and computer vision

作者： Cheng, Suyan Zhang, Feifei Zhou, Haoliang Xu, Changsheng Tianjin Univ Technol Tianjin Peoples R China Jiangsu Univ Sci & Technol Zhenjiang Jiangsu Peoples R China Chinese Acad Sci Inst Automat Beijing Peoples R China

ISBN: (纸本)9789819786190;9789819786206

In image classification tasks, visual appearance is generally considered as a crucial cue for understanding images. However, relying solely on visual information can lead to misclassification in fine-grained image classification tasks. Multi-modal knowledge has been proven to provide critical cues for various computer vision tasks, such as image retrieval and vision question answering. In this paper, we integrate multi-modal knowledge into visual features to enhance the model's understanding of visual content and accomplish fine-grained image classification tasks. Specifically, we adopt an effective visual enhanced module to capture global and local features, obtaining discriminative visual representations. Meanwhile, we employ knowledge distillation to transfer multi-modal knowledge from the Contrastive Language-Image Pretraining (CLIP) model to our model, improving its generalization ability. Moreover, we incorporate scene text into our visual features to provide richer contextual information. Experiments on the Con-Text, Drink Bottle, and Crowd Activity benchmark datasets demonstrate that our approach achieves 5.41%, 1.2%, and 7.55% improvements in mAP compared to the current state-of-the-art methods, respectively.

关键词： Fine-grained image classification vision transformer Scene text

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Enhancing Semi-Dense Feature Matching through Probabilistic Modeling of Cascaded Supervision and Consistency 7th

Enhancing Semi-Dense Feature Matching Through Probabilistic ...

引用

7th chinese conference on pattern recognition and computer vision

作者： Min, Hongchang Tang, Yihong Li, Qiankun Wang, Zengfu Chinese Acad Sci Hefei Inst Phys Sci Hefei 230031 Peoples R China Univ Sci & Technol China Hefei 230026 Peoples R China Natl Engn Res Ctr Speech & Language Informat Proc Hefei Peoples R China

ISBN: (纸本)9789819784981;9789819784998

Local feature matching, which identifies correspondences between image pairs, remains a fundamental challenge in computer vision. Current methods usually utilize multi-scale feature fusion to refine reference areas and filter out irrelevant features. However, relying solely on agent loss for supervising upper-level features can reduce refinement accuracy. In addition, the variance in significance among features within the reference region is often overlooked. In this paper, we propose an approach termed Cascaded Supervision-Neighborhood ConsistencyProbabilistic Modeling that generates more accurate reference ranges for feature matching. Specifically, the proposed method first directs cascading supervision of the matching results at various scales, enabling more precise refinement of regions. then, it aggregates matching results at each scale to maintain neighborhood consistency. Finally, probabilistic modeling of the refined reference region is employed, focusing more on relevant features. Extensive experiments conducted on four popular benchmarks demonstrate that our method achieves state-of-the-art and comparable performance.

关键词： Feature matching Cascade networks Neighborhood consistency Probabilistic modeling

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VS-LLM: Visual-Semantic Depression Assessment Based on LLM for Drawing Projection Test 7th

VS-LLM: Visual-Semantic Depression Assessment Based on LLM f...

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7th chinese conference on pattern recognition and computer vision

作者： Wu, Meiqi Kang, Yaxuan Li, Xuchen Hu, Shiyu Chen, Xiaotang Kang, Yunfeng Wang, Weiqiang Huang, Kaiqi Univ Chinese Acad Sci Sch Comp Sci & Technol Beijing Peoples R China Chinese Acad Sci Inst Automat Beijing Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing Peoples R China CAS Ctr Excellence Brain Sci & Intelligence Techn Shanghai Peoples R China

ISBN: (纸本)9789819786916;9789819786923

the Drawing Projection Test (DPT) is an essential tool in art therapy, allowing psychologists to assess participants' mental states through their sketches. Specifically, through sketches with the theme of "a person picking an apple from a tree (PPAT)", it can be revealed whether the participants are in mental states such as depression. Compared with scales, the DPT can enrich psychologists' understanding of an individual's mental state. However, the interpretation of the PPAT is laborious and depends on the experience of the psychologists. To address this issue, we propose an effective identification method to support psychologists in conducting a large-scale automatic DPT. Unlike traditional sketch recognition, DPT more focus on the overall evaluation of the sketches, such as color usage and space utilization. Moreover, PPAT imposes a time limit and prohibits verbal reminders, resulting in low drawing accuracy and a lack of detailed depiction. To address these challenges, we propose the following efforts: (1) Providing an experimental environment for automated analysis of PPAT sketches for depression assessment;(2) Offering a Visual-Semantic depression assessment based on LLM (VS-LLM) method;(3) Experimental results demonstrate that our method improves by 17.6% compared to the psychologist assessment method. We anticipate that this work will contribute to the research in mental state assessment based on PPAT sketches' elements recognition. Our datasets and codes are available at: https://***/wmeiqi/VS-LLM.

关键词： Drawing projection test Art therapy LLM Multimodal depression assessment

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Single Model Learns Multiple Styles of chinese Calligraphy via Style Collection Mechanism 7th

Single Model Learns Multiple Styles of Chinese Calligraphy v...

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7th chinese conference on pattern recognition and computer vision

作者： Dong, Zhiqiang Xiao, Yun Duan, JiaShun Wang, Xuanhong Xu, Pengfei Zheng, Xia Northwest Univ Sch Informat Sci & Technol Xian Peoples R China Xian Univ Posts & Telecommun Xian Peoples R China Zhejiang Univ Hangzhou Zhejiang Peoples R China

ISBN: (纸本)9789819784899;9789819784905

the automatic generation of chinese calligraphy images is a very challenging task, because the structure of chinese characters is very complex. At present, most methods learn the style of the images one by one, meaning that they lack the ability to model the style of the calligraphy from a more macro perspective. To solve these problems, this paper proposes a one-to-many style transfer model, SCGAN, based on a style collection mechanism. Our model can gather information from the collection level to complete the task of chinese character image generation. the main features of our model are as follows: first, based on the proposed style collection mechanism, our model can collect and transform style features from the collection level;second, we redesigned the structure of the generative adversarial network. Our model can complete the one-to-many style transfer task, which can greatly reduce the workload associated with multi-target style transfer. Compared with other deep learning methods, the results obtained by our method are higher quality and closer to reality. Experimental results show that our method achieves better performance than other methods in one-to-one and one-to-many chinese character generation tasks.

关键词： Style transfer chinese character generation Virtual restoration of calligraphy

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An Asymmetric Game theoretic Learning Model 7th

An Asymmetric Game Theoretic Learning Model

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7th chinese conference on pattern recognition and computer vision

作者： Yin, Qiyue Yu, Tongtong Feng, Xueou Yang, Jun Huang, Kaiqi 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)9789819785018;9789819785025

Recent successes of game AIs such as AlphaGo and AlphaStar, which beat professional human players in the games Go and StarCraft, respectively, mark the breakthroughs of intelligent decision making technique in complex games. Generally, games studied previously are mostly symmetric in game-theoretic sense due to their sports or e-sports characteristics. However, games in reality are usually asymmetric because of the position-dependent resource unbalance, and they are rarely studied. In this paper, we propose a novel asymmetric game model based on the framework of game theoretic learning. Specifically, we develop an agent training method with three steps: game model formulation, solution concept definition and game solution computation. To verify our model, a mini-Wargame is used in our experiment, where the initial number and visual scope are set to be unbalanced. Experiments show that the proposed method is better than popular self-play based methods such as naive self-play and prioritized fictitious self-play. the work provides a game-theoretic view for asymmetric games, and it may attract more interests for the rarely studied asymmetric games.

关键词： Game theoretic learning Self-play Deep reinforcement learning

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HHATP: A Lightweight Heterogeneous Hierarchical Attention Model for Trajectory Prediction 7th

HHATP: A Lightweight Heterogeneous Hierarchical Attention Mo...

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7th chinese conference on pattern recognition and computer vision

作者： Lai, Zeyu Zhu, Xingliang Yang, Chunmeng Kong, Bin Chinese Acad Sci Hefei Inst Phys Sci Hefei 230031 Peoples R China Univ Sci & Technol China Hefei 230026 Peoples R China Anhui Engn Lab Intelligent Driving Technol & Appl Hefei 230031 Peoples R China

ISBN: (纸本)9789819787913;9789819787920

Predicting the future trajectories of agents in complex traffic scene is one of the key issues in autonomous driving, requiring reliable and effective predictions for all agents in the scene. Existing trajectory prediction models have achieved high performance on public datasets, but deploying models on vehicles requires both high accuracy and fast computation. It is necessary to balance the complexity of computation and the effectiveness of the structure when designing model. To address the above problem, we proposes a lightweight trajectory prediction model HHATP. Our method is scene-centric and located in the same coordinate system. We use different encoders for the heterogeneous scene objects and the encoded results are then fed into a hierarchical attention module, which considers both global and local interaction to model the relationships between elements. Subsequently, a dynamic weight decoder is used to obtain the trajectories of all agents. Our method achieves good accuracy on the Argoverse dataset and enables fast inference.

关键词： Attention mechanism hierarchical structure heterogeneous information trajectory prediction

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Semi-Supervised Camouflaged Object Detection: Multi Information Fusion Combined with Adaptive Receptive Field Selection Network 7th

Semi-Supervised Camouflaged Object Detection: Multi Informat...

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7th chinese conference on pattern recognition and computer vision

作者： Yang, Guang Xiao, Feng Liu, Ruyu Zhang, Jiawei Zhang, Jianhua Chen, Shengyong Tianjin Univ Technol Tianjin 300380 Peoples R China Hangzhou Normal Univ Hangzhou 311121 Peoples R China

ISBN: (纸本)9789819788576;9789819788583

Camouflaged object detection is focused on segmenting objects concealed within their surroundings. this technology can be applied in various fields such as medical image analysis, wildlife conservation, autonomous driving, and others. Existing semi-supervised camouflage object detection methods often suffer from poor network performance due to the accumulation of incorrect pseudo labels, and they fail to fully utilize multi-scale features or account for the diverse scale contexts necessary for various sizes of camouflage objects. In this paper, we propose an innovative semi-supervised learning strategy. We employ a dual-branch network named CAMNet, utilizing salient maps corresponding to camouflage objects to aid detection. We also introduce a Multi-Information Fusion Feature Perception module (MIF) and an Adaptive Receptive Field Selection module (ARFS), which are integrated into the network. Ultimately, we perform thorough comparative experiments on the R2C7K, COD-Water, and COD-Jungle datasets, showcasing superior performance in contrast to current state-of-the-art methods. We also conduct ablation experiments, further confirming the effectiveness of the proposed modules.

关键词： Camouflaged object detection Semi-supervised learning computer vision Deep learning

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Centroid-Centered Modeling for Efficient vision Transformer Pre-Training 7th

Centroid-Centered Modeling for Efficient Vision Transformer ...

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7th chinese conference on pattern recognition and computer vision

作者： Van, Xin Li, Zuchao Zhang, Lefei Wuhan Univ Wuhan Peoples R China

ISBN: (纸本)9789819785049;9789819785056

Masked Image Modeling (MIM) is a new self-supervised vision pre-training paradigm using vision Transformer (ViT). Previous works can be pixel-based or token-based, using original pixels or discrete visual tokens from parametric tokenizer models, respectively. Our proposed centroid-based approach, CCViT, leverages k-means clustering to obtain centroids for image modeling without supervised training of the tokenizer model, which only takes seconds to create. this non-parametric centroid tokenizer only takes seconds to create and is faster for token inference. the centroids can represent both patch pixels and index tokens with the property of local invariance. Specifically, we adopt patch masking and centroid replacing strategies to construct corrupted inputs, and two stacked encoder blocks to predict corrupted patch tokens and reconstruct original patch pixels. Experiments show that our CCViT achieves 84.4% top-1 accuracy on ImageNet-1K classification with ViT-B and 86.0% with ViT-L. We also transfer our pre-trained model to other downstream tasks. Our approach achieves competitive results with recent baselines without external supervision and distillation training from other models.

关键词： Large-scale visual pre-training Self-supervised learning Masked image modeling

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