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检索条件"任意字段=6th IAPR International Conference on Pattern Recognition in Bioinformatics"

共 1867 条记录，以下是31-40 订阅

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External Prompt Features Enhanced Parameter-Efficient Fine-Tuning for Salient Object Detection 27th

External Prompt Features Enhanced Parameter-Efficient Fine-T...

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27th international conference on pattern recognition, ICPR 2024

作者： Liang, Wen Ran, Peipei Bai, Mengchao Liu, Xiao Githinji, P. Bilha Zhao, Wei Qin, Peiwu Tsinghua Shenzhen International Graduate School Tsinghua University Shenzhen China Central Media Technology Institute Shenzhen Huawei China

Salient object detection (SOD) aims at finding the60;most salient objects in images and outputs pixel-level binary masks. Transformer-based methods achieve promising performance due to60;their global semantic un... 详细信息

ISBN: (纸本)9783031783463

Salient object detection (SOD) aims at finding the most salient objects in images and outputs pixel-level binary masks. Transformer-based methods achieve promising performance due to their global semantic understanding, crucial for identifying salient objects. However, these models tend to be large and require numerous training parameters. To better harness the potential of transformers for SOD, we propose a novel parameter-efficient fine-tuning method aimed at reducing the number of training parameters while enhancing the salient object detection capability. Our model, termed EXternal Prompt features Enhanced adapteRTuning (ExPert), features an encoder-decoder structure with adapters and injectors interspersed between the layers of a frozen transformer encoder. the adapter modules adapt the pre-trained backbone to SOD while the injector modules incorporate external prompt features to enhance the awareness of salient objects. Comprehensive experiments demonstrate the superiority of our method. Surpassing former state-of-the-art (SOTA) models across five SOD datasets, ExPert achieves 0.215 mean absolute error (MAE) in the ECSSD dataset with 80.2 M trained parameters, 21% better than SelfReformer [31] and 47% better than EGNet [33]. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Semantics

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BADA-LAT: Efficient Local Attention Transformer for Chinese Named Entity recognition with Boundary and LLM-Based Data Augmentation 27th

BADA-LAT: Efficient Local Attention Transformer for Chinese ...

引用

27th international conference on pattern recognition, ICPR 2024

作者： Qiu, Xiaoping Yang, Ke Du, Shiling School of Computing and Artificial Intelligence Southwest Jiaotong University No. 999 Xi’an Road Sichuan Chengdu611756 China Tangshan Institute of Southwest Jiaotong University No. 6 Jingqu Road Hebei Tangshan063000 China National United Engineering Laboratory of Integrated and Intelligent Transportation No. 111 North Section 1 Second Ring Road Sichuan Chengdu610031 China Sichuan Key Laboratory of Manufacturing Industry Chain Collaboration and Information Support Technology No. 999 Xi’an Road Sichuan Chengdu610016 China Engineering Research Center of Sustainable Urban Intelligent Transportation No. 999 Xi’an Road Sichuan Chengdu611756 China Sichuan Institute of Industrial Software Technology No. 61 Sha’wan Road Sichuan Chengdu610031 China

Progress in Chinese Named Entity recognition (CNER)60;has highlighted lexicon-based methods that use word information to60;boost performance. However, these methods neglect two crucial aspects:60;the regulari... 详细信息

ISBN: (纸本)9783031784941

Progress in Chinese Named Entity recognition (CNER) has highlighted lexicon-based methods that use word information to boost performance. However, these methods neglect two crucial aspects: the regularity of word boundary and the characteristics of the NER task. To address these shortcomings, we introduce BADA-LAT, a Transformer architecture that incorporates word boundary information and introduce local attention computation. It can enhance entity boundary recognition and concentrate the attention weight of the target token on adjacent characters and matched words. Furthermore, to mitigate the issue of class imbalance, we augment the original training data using large language model (LLM). Our method outperforms other lexicon-based ones, as shown in experiments on four Chinese datasets. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： boundary augmentation Chinese NER data augmentation local attention

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A Comprehensive Review of Machine Learning and Deep Learning Techniques for Rice Leaf Disease Classification 6th

A Comprehensive Review of Machine Learning and Deep Learning...

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6th international conference on Computational Intelligence in pattern recognition, CIPR 2024

作者： Tabassum, Tasnim Rafiq, Shifat Ara Zulfiker, Md. Sabab Ritu, Nadia Afrin Biswas, Al Amin Department of CSE Daffodil International University Dhaka Bangladesh Department of CSE Sheikh Hasina University Netrokona Bangladesh Department of Computer Science and Engineering Bangabandhu Sheikh Mujibur Rahman University Kishoreganj Bangladesh Department of CSE Jahangirnagar University Dhaka Bangladesh

ISBN: (纸本)9789819780891

Rice is one of the most significant primary crops and provides a substantial portion of the world’s food for billions of people worldwide. However, several leaf-related diseases pose significant risks to the rice crop’s yield and quality. the traditional approaches to disease identification and classification are frequently arduous and require specialized expertise. the domain of machine learning (ML), deep learning (DL), and the convergence of both of these techniques has emerged as a potential approach that can address this issue and enable immediate and accurate diagnosis of rice leaf disease. Additionally, this study offers a thorough analysis and contrast different approaches, focusing on addressing the initial research questions and identifying potential future possibilities. this study functions as a comprehensive resource for research groups engaged in the development of ML and DL applications specifically targeted at the categorization of rice leaf diseases. In addition to aiding various studies in creating feasible applications based on ML and DL techniques, this review effort will instruct the researchers on how these methods might facilitate the identification of rice leaf disease at an early stage. the insights derived from the study offer useful direction for the advancement of research related to rice leaf diseases. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

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Adaptive Global Gesture Paths and Signature Features for Skeleton-based Gesture recognition 27th

Adaptive Global Gesture Paths and Signature Features for Ske...

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27th international conference on pattern recognition, ICPR 2024

作者： Shi, Dongzi Zhang, Xin Cheng, Jiale Xiong, Tong Ni, Hao South China University of Technology Guangdong Guangzhou510000 China University of North Carolina at Chapel Hill Chapel HillNC27514 United States University College London LondonWC1E6BT United Kingdom

ISBN: (纸本)9783031783531

Gestures exhibit sparse joint variations and different time scales, making local dynamic analysis and global spatio-temporal modeling important. Path signature provides mathematical and dynamic analysis of joint trajectories to assist in spatio-temporal modeling. However, previous methods relied on predefined local spatio-temporal joint trajectories, also known as paths. this limitation makes it challenging to directly capture the dynamics of the entire gesture and adapt to varying scales of gesture changes. In this work, we construct the Adaptive Global Gesture Path and extract its signature features as gesture representations. Specifically, we designed global branch to model the global spatio-temporal variation relationship of joints. the dynamic branch is based on the proposed Motion Guided Cluster Attention Block, which emphasizes joints exhibiting similar motion patterns. Combining two branches, the predicted dynamic and global score can distinguish key joints at different times to construct the Adaptive Global Gesture Path that condensely represents the entire gesture. We conducted experiments on the ChaLearn2013 and WLASL datasets, and achieved the state-of-the-art results with much smaller model size. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Gesture recognition

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Deep Learning in Automated Worm Identification and Tracking for C. Elegan Mating Behaviour Analysis 27th

Deep Learning in Automated Worm Identification and Trackin...

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27th international conference on pattern recognition, ICPR 2024

作者： Akpu, Chukwuma Hilary Wei, Hong Hong, Xia University of Reading ReadingRG6 6AY United Kingdom

ISBN: (纸本)9783031781216

this study is concerned with computer vision technologies applied in C. elegans (diminutive nematodes) mating behaviour analysis, more specifically object detection and tracking to find contacts of male and female worms in worm mating videos. Advanced deep learning algorithms, such as YOLOv8 and DeepSORT, are adapted in the automated worm identification and tracking system. A modified DeepSORT algorithm is developed to cope with appearance similarity of C. elegans for improving the tracking accuracy. In addition, a male worm detection and tracking algorithm, utilising the male worm’s mobility characteristic, assists the modified DeepSORT in accurate male worm tracking. Finally, worm contact detection is implemented by calculating the Euclidean distance between the male and female worms. the developed system, named as M1 and M2, is trained and evaluated under two sets of data, bounding boxes and segmented worms, respectively. Furthermore, we compared the effectiveness of including SAM segmentation optional module in experiments. the evaluation results have shown that YOLOv8 has excellent performance in worm detection to cope with deformable worm shape, and the modified DeepSORT significantly outperforms the default DeepSORT in worm tracking. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Contrastive Learning

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Incremental Object 6D Pose Estimation 27th

Incremental Object 6D Pose Estimation

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27th international conference on pattern recognition, ICPR 2024

作者： Tian, Long Sorrenti, Amelia Pang, Yik Lung Bellitto, Giovanni Palazzo, Simone Spampinato, Concetto Oh, Changjae Queen Mary University of London London United Kingdom Southwest Jiaotong University Chengdu China University of Catania Catania Italy

ISBN: (纸本)9783031783944

We present a novel setting for 6D object pose estimation, where a model progressively adapts its parameters to estimate the pose of new objects without forgetting. this capability is crucial for real-world applications, particularly in scenarios where a deployed model must accommodate new objects while mitigating the risk of forgetting previously seen objects. To tackle this challenge, we propose a replay-based incremental learning technique designed to retain key information about previously seen objects when the model is exposed to a new one. Our approach relies on a memory buffer comprising keyframes of previously encountered objects, serving to regularize the model parameters based on past experiences while allowing for the update of model features to perform pose estimation on new objects. We validate the effectiveness of our method on the standard Linemod and YCB-Video datasets, demonstrating how our method surpasses baseline approaches in incremental learning at the task at hand. the project website is available at: https://***/ILPose. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Contrastive Learning

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2by2: Weakly-Supervised Learning for Global Action Segmentation 27th

2by2: Weakly-Supervised Learning for Global Action Segmenta...

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27th international conference on pattern recognition, ICPR 2024

作者： Bueno-Benito, Elena Dimiccoli, Mariella Institut de Robòtica i Informàtica Industrial CSIC-UPC Llorens i Artigas 4-6 Barcelona08028 Spain

ISBN: (纸本)9783031781247

this paper presents a simple yet effective approach for the poorly investigated task of global action segmentation, aiming at grouping frames capturing the same action across videos of different activities. Unlike the case of videos depicting all the same activity, the temporal order of actions is not roughly shared among all videos, making the task even more challenging. We propose to use activity labels to learn, in a weakly-supervised fashion, action representations suitable for global action segmentation. For this purpose, we introduce a triadic learning approach for video pairs, to ensure intra-video action discrimination, as well as inter-video and inter-activity action association. For the backbone architecture, we use a Siamese network based on sparse transformers that takes as input video pairs and determine whether they belong to the same activity. the proposed approach is validated on two challenging benchmark datasets: Breakfast and YouTube Instructions, outperforming state-of-the-art methods. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Self-supervised learning

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CrackUDA: Incremental Unsupervised Domain Adaptation for Improved Crack Segmentation in Civil Structures 27th

CrackUDA: Incremental Unsupervised Domain Adaptation for Im...

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27th international conference on pattern recognition, ICPR 2024

作者： Srivastava, Kushagra Kancharla, Damodar Datta Tahereen, Rizvi Ramancharla, Pradeep Kumar Sarvadevabhatla, Ravi Kiran Kandath, Harikumar International Institute of Information Technology Hyderabad India

ISBN: (纸本)9783031781124

Crack segmentation plays a crucial role in ensuring the structural integrity and seismic safety of civil structures. However, existing crack segmentation algorithms encounter challenges in maintaining accuracy with domain shifts across datasets. To address this issue, we propose a novel deep network that employs incremental training with unsupervised domain adaptation (UDA) using adversarial learning, without a significant drop in accuracy in the source domain. Our approach leverages an encoder-decoder architecture, consisting of both domain-invariant and domain-specific parameters. the encoder learns shared crack features across all domains, ensuring robustness to domain variations. Simultaneously, the decoder’s domain-specific parameters capture domain-specific features unique to each domain. By combining these components, our model achieves improved crack segmentation performance. Furthermore, we introduce BuildCrack, a new crack dataset comparable to sub-datasets of the well-established CrackSeg9K dataset in terms of image count and crack percentage. We evaluate our proposed approach against state-of-the-art UDA methods using different sub-datasets of CrackSeg9K and our custom dataset. Our experimental results demonstrate a significant improvement in crack segmentation accuracy and generalization across target domains compared to other UDA methods - specifically, an improvement of 0.65 and 2.7 mIoU on source and target domains respectively. Additional details and code can be accessed from https://***. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Unsupervised learning

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Developing an IoT-Based Surface Electromyography Signal Detection Device for Sarcopenia 6th

Developing an IoT-Based Surface Electromyography Signal Dete...

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6th international conference on Biomedical and Health Informatics, ICBHI 2024

作者： Wang, Po-Xiang Jiang, Ching-Fen Program of Smart Healthcare and Bioinformatics I-Shou University Kaohsiung City Taiwan

ISBN: (纸本)9783031863226

Sarcopenia is characterized by a persistent reduction in skeletal muscle mass and function, which, if undetected and untreated, can lead to serious consequences such as increased fall risk and long-term immobility. Although sarcopenia has gained attention in recent years, there are currently no effective pharmacological treatments. Muscle loss can only be mitigated in the early stages through resistance training and nutritional interventions. However, precise detection methods used in hospitals are often costly and inaccessible to the general population. Traditional screening methods, like the Sarcopenia Self-Screening Questionnaire (SARC-F), proposed by the Asian Working Group for Sarcopenia, rely on subjective patient reports and lack objectivity. this study aims to address this gap by developing a rapid and convenient sarcopenia screening device that can be used at home or in community settings. Leveraging Internet of things (IoT) technology, we developed a surface electromyography (sEMG) system to measure the muscle activity of the gastrocnemius, tibialis anterior, and quadriceps during walking and standing. the muscle signals were wirelessly transmitted using the MQTT protocol for backend analysis, enabling real-time gait and muscle strength monitoring. Twenty-eight healthy elderly individuals from the community participated in this study. To validate the effectiveness of the device, traditional sarcopenia-related physical measurements were obtained using standard methods. We first classified participants into four sarcopenia risk levels using the above data. In the meantime, we used deep image gait analysis software (GaitBEST) to classify it into four risk levels. Preliminary results show that these two classification levels had a slight discrepancy. However, the sEMG signals obtained using the developed device reflect the risk level determined by gait pattern, indicating the necessity of further analysis to explore the relation between gait and skeletal muscl

关键词： Electromyography

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ConCSE: Unified Contrastive Learning and Augmentation for Code-Switched Embeddings 27th

ConCSE: Unified Contrastive Learning and Augmentation for ...

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27th international conference on pattern recognition, ICPR 2024

作者： Jeon, Jangyeong Cho, Sangyeon Ma, Minuk Kim, Junyeong Department of Artificial Intelligence Chung-Ang University Seoul06974 Korea Republic of Department of Computer Science University of British Columbia VancouverBCV6T 1Z4 Canada

this paper examines the Code-Switching (CS) phenomenon where two languages intertwine within a single utterance.60;there exists a noticeable need for research on the CS between English60;and Korean. We highlight... 详细信息

ISBN: (纸本)9783031784972

this paper examines the Code-Switching (CS) phenomenon where two languages intertwine within a single utterance. there exists a noticeable need for research on the CS between English and Korean. We highlight that the current Equivalence Constraint (EC) theory for CS in other languages may only partially capture English-Korean CS complexities due to the intrinsic grammatical differences between the languages. We introduce a novel Koglish dataset tailored for English-Korean CS scenarios to mitigate such challenges. First, we constructed the Koglish-GLUE dataset to demonstrate the importance and need for CS datasets in various tasks. We found the differential outcomes of various foundation multilingual language models when trained on a monolingual versus a CS dataset. Motivated by this, we hypothesized that SimCSE, which has shown strengths in monolingual sentence embedding, would have limitations in CS scenarios. We construct a novel Koglish-NLI (Natural Language Inference) dataset using a CS augmentation-based approach to verify this. From this CS-augmented dataset Koglish-NLI, we propose a unified contrastive learning and augmentation method for code-switched embeddings, ConCSE, highlighting the semantics of CS sentences. Experimental results validate the proposed ConCSE with an average performance enhancement of 1.77% on the Koglish-STS(Semantic Textual Similarity) tasks. (Source code available at https://***/jjy961228/ConCSE). © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Contrastive Learning

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