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32nd ACM International Conference on Multimedia, MM 2024

作者： Deng, Chenghao Xu, Haote Chen, Xiaolu Xu, Haodi Tu, Xiaotong Ding, Xinghao Huang, Yue Institute of Artificial Intelligence Xiamen University Xiamen China School of Informatics Xiamen University Xiamen China School of Informatics Xiamen University Key Laboratory of Multimedia Trusted Perception and Efficient Computing Xiamen China

ISBN: (纸本)9798400706868

Recently, large pre-trained vision-language models, such as CLIP, have demonstrated significant potential in zero-/few-shot anomaly detection tasks. However, existing methods not only rely on expert knowledge to manually craft extensive text prompts but also suffer from a misalignment of high-level language features with fine-level vision features in anomaly segmentation tasks. In this paper, we propose a method, named SimCLIP, which focuses on refining the aforementioned misalignment problem through bidirectional adaptation of both Multi-Hierarchy Vision Adapter (MHVA) and Implicit Prompt Tuning (IPT). In this way, our approach requires only a simple binary prompt to efficiently accomplish anomaly classification and segmentation tasks in zero-shot scenarios. Furthermore, we introduce its few-shot extension, SimCLIP+, integrating the relational information among vision embeddings and skillfully merging the cross-modal synergy information between vision and language to address downstream anomaly detection tasks. Extensive experiments on two challenging datasets prove the more remarkable generalization capacity of our method compared to the current SOTA approaches. Our code is available at https://***/CH-ORGI/SimCLIP. © 2024 ACM.

关键词： Zero-shot learning

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Carrying Out CNN Channel Pruning in a White Box

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IEEE Transactions on Neural Networks and Learning Systems 2023年第10期34卷 7946-7955页

作者： Zhang, Yuxin Lin, Mingbao Lin, Chia-Wen Chen, Jie Wu, Yongjian Tian, Yonghong Ji, Rongrong Xiamen University Media Analytics and Computing Laboratory Department of Artificial Intelligence School of Informatics Xiamen361005 China Youtu Laboratory Tencent Shanghai200233 China National Tsing Hua University Department of Electrical Engineering The Institute of Communications Engineering Hsinchu30013 Taiwan Peng Cheng Laboratory Shenzhen518000 China Peking University Department of Computer Science and Technology Beijing100871 China School of Informatics and the Institute of Artificial Intelligence Xiamen University Media Analytics and Computing Laboratory Department of Artificial Intelligence Xiamen361005 China

Channel pruning has been long studied to compress convolutional neural networks (CNNs), which significantly reduces the overall computation. Prior works implement channel pruning in an unexplainable manner, which tends to reduce the final classification errors while failing to consider the internal influence of each channel. In this article, we conduct channel pruning in a white box. Through deep visualization of feature maps activated by different channels, we observe that different channels have a varying contribution to different categories in image classification. Inspired by this, we choose to preserve channels contributing to most categories. Specifically, to model the contribution of each channel to differentiating categories, we develop a class-wise mask for each channel, implemented in a dynamic training manner with respect to the input image's category. On the basis of the learned class-wise mask, we perform a global voting mechanism to remove channels with less category discrimination. Lastly, a fine-tuning process is conducted to recover the performance of the pruned model. To our best knowledge, it is the first time that CNN interpretability theory is considered to guide channel pruning. Extensive experiments on representative image classification tasks demonstrate the superiority of our White-Box over many state-of-the-arts (SOTAs). For instance, on CIFAR-10, it reduces 65.23% floating point operations per seconds (FLOPs) with even 0.62% accuracy improvement for ResNet-110. On ILSVRC-2012, White-Box achieves a 45.6% FLOP reduction with only a small loss of 0.83% in the top-1 accuracy for ResNet-50. Code is available at https://***/zyxxμWhite-Box. © 2012 IEEE.

关键词： Visualization

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GCAT: graph calibration attention transformer for robust object tracking

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Neural computing and Applications 2024年第23期36卷 14151-14172页

作者： Chen, Si Hu, Xinxin Wang, Da-Han Yan, Yan Zhu, Shunzhi Fujian Key Laboratory of Pattern Recognition and Image Understanding School of Computer and Information Engineering Xiamen University of Technology Xiamen361024 China State Key Laboratory of Integrated Services Networks Xidian University Xi’an710071 China Fujian Key Laboratory of Sensing and Computing for Smart City School of Informatics Xiamen University Xiamen361005 China

Recent Siamese trackers have taken advantage of transformers to achieve impressive advancements. However, existing transformer trackers ignore considering the positional and structural information between tokens, and traditional template update strategies easily introduce noises to the dynamic templates during tracking. In order to alleviate this issue, this paper develops a novel end-to-end graph calibration attention transformer network (GCAT) to enhance tracking robustness and accuracy. A graph calibration attention mechanism is first designed to calibrate and aggregate template information, for effectively updating dynamic templates during the tracking process. Specifically, each token is considered as a node in the graph, and then, we calculate the weight relationships between each node and their adjacent nodes. Thus, this mechanism can aggregate the global context information of the template and search nodes and activate feature channels based on weights and biases to obtain more discriminative feature information. Moreover, we leverage a multi-level dropout mechanism to perform the data dropout, the layer dropout, and the feature dropout on the data, network, and attention levels, respectively, to avoid overfitting of local-specific information and improve the generalization ability. Extensive experiments show the proposed method achieves superior performance on seven challenging benchmark datasets, i.e., OTB100, OTB2013, UAV123, LaSOT, GOT10K, VOT2020, and TrackingNet. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Aggregates

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A Cross-Domain Ontology Semantic Representation Based on NCBI-BlueBERT Embedding

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Chinese Journal of Electronics 2022年第5期31卷 860-869页

作者： ZHAO Lingling WANG Junjie WANG Chunyu GUO Maozu Faculty of Computing Harbin Institute of Technology Department of Medical Informatics School of Biomedical Engineering and InformaticsNanjing Medical University Beijing Key Laboratory of Intelligent Processing for Building Big Data School of Electrical and Information Engineering Beijing University of Civil Engineering and Architecture

A common but critical task in biological ontologies data analysis is to compare the difference between ontologies. There have been numerous ontologybased semantic-similarity measures proposed in specific ontology domain, but it still remains a challenge for crossdomain ontologies comparison. An ontology contains the scientific natural language description for the corresponding biological aspect. Therefore, we develop a new method based on natural language processing(NLP) representation model bidirectional encoder representations from transformers(BERT) for cross-domain semantic representation of biological ontologies. This article uses the BERT model to represent the word-level of the ontologies as a set of vectors, facilitating the semantic analysis or comparing the biomedical entities named in an ontology or associated with ontology terms. We evaluated the ability of our method in two experiments: calculating similarities of pair-wise disease ontology and human phenotype ontology terms and predicting the pair-wise of proteins interaction. The experimental results demonstrated the comparative performance. This gives promise to the development of NLP methods in biological data analysis.

关键词： human phenotype ontology cross-domain semantic representation natural language description pair-wise disease ontology data analysis bidirectional encoder representations NLP protein interaction diseases proteins natural language processing ontologies (artificial intelligence) natural language processing representation neural nets bioinformatics semantic-similarity measures cross-domain ontology semantic representation semantic analysis NCBI-BlueBERT embedding biological ontology data analysis

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Predicting game-induced emotions using EEG, data mining and machine learning

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Bulletin of the National Research Centre 2024年第1期48卷 1-10页

作者： Lim, Min Xuan Teo, Jason Faculty of Computing and Informatics Universiti Malaysia Sabah Kota Kinabalu Malaysia Creative Advanced Machine Intelligence Research Centre Faculty of Computing and Informatics Universiti Malaysia Sabah Kota Kinabalu Malaysia Evolutionary Computing Laboratory Faculty of Computing and Informatics Universiti Malaysia Sabah Kota Kinabalu Malaysia

Emotion is a complex phenomenon that greatly affects human behavior and thinking in daily life. Electroencephalography (EEG), one of the human physiological signals, has been emphasized by most researchers in emotion recognition as its specific properties are closely associated with human emotion. However, the number of human emotion recognition studies using computer games as stimuli is still insufficient as there were no relevant publicly available datasets provided in the past decades. Most of the recent studies using the Gameemo public dataset have not clarified the relationship between the EEG signal’s changes and the emotion elicited using computer games. Thus, this paper is proposed to introduce the use of data mining techniques in investigating the relationships between the frequency changes of EEG signals and the human emotion elicited when playing different kinds of computer games. The data acquisition stage, data pre-processing, data annotation and feature extraction stage were designed and conducted in this paper to obtain and extract the EEG features from the Gameemo dataset. The cross-subject and subject-based experiments were conducted to evaluate the classifiers’ performance. The top 10 association rules generated by the RCAR classifier will be examined to determine the possible relationship between the EEG signal's frequency changes and game-induced emotions. The RCAR classifier constructed for cross-subject experiment achieved highest accuracy, precision, recall and F1-score evaluated with over 90% in classifying the HAPV, HANV and LANV game-induced emotions. The 20 experiment cases’ results from subject-based experiments supported that the SVM classifier could accurately classify the 4 emotion states with a kappa value over 0.62, demonstrating the SVM-based algorithm’s capabilities in precisely determining the emotion label for each participant’s EEG features’ instance. The findings in this study fill the existing gap of game-induced emotion recog

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GLAC-GCN: Global and Local Topology-Aware Contrastive Graph Clustering Network

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年第6期6卷 1448-1459页

作者： Xu, Yuan-Kun Huang, Dong Wang, Chang-Dong Lai, Jian-Huang South China Agricultural University College of Mathematics and Informatics Guangzhou510642 China Sun Yat-sen University School of Computer Science and Engineering Guangzhou510006 China Guangdong Key Laboratory of Information Security Technology Guangzhou510006 China Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education Guangzhou510006 China

Though many deep attributed graph clustering approaches have been developed in recent years, most still suffer from two limitations. First, in the input space, they primarily rely on the original topology structure as the input (to some graph network), lacking the ability to jointly leverage local and global topology information to refine the graph. Second, in the learning process, they usually employ a single graph learning pipeline (with a single input graph), overlooking the opportunities in the joint optimization of multiple graph learning pipelines (with multiple topology structures). In view of this, this article presents a global and local topology-aware contrastive graph clustering network (GLAC-GCN) for attributed graph clustering. Specifically, the local topology structure, and global semantic information are simultaneously utilized to refine the graph. Then a paralleled graph convolutional network (GCN) learning mechanism is designed, where i) both the original graph and the globally and locally refined graph are treated as input graphs, and ii) two pipelines of GCNs are jointly and interactively utilized. Furthermore, a self-adaptive learning mechanism is devised to ensure consistency between multiple learning pipelines via the Kullback–Leibler (KL)-divergence. Meanwhile, the contrastive learning is enforced by minimizing the mismatch of the cluster distributions obtained from different GCN pipelines. Extensive experiments are conducted on seven real-world datasets. Notably, GLAC-GCN achieves the best ACC (or NMI) scores on all (or five) of the seven datasets, demonstrating its superiority over the state-of-the-art approaches. © 2020 IEEE.

关键词： Semantics

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P2SAM: Probabilistically Prompted SAMs Are Efficient Segmentator for Ambiguous Medical Images 24

P2SAM: Probabilistically Prompted SAMs Are Efficient Segment...

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32nd ACM International Conference on Multimedia, MM 2024

作者： Huang, Yuzhi Li, Chenxin Lin, Zixu Liu, Hengyu Xu, Haote Liu, Yifan Huang, Yue Ding, Xinghao Tu, Xiaotong Yuan, Yixuan School of Informatics Xiamen University China The Chinese University of Hong Kong Hong Kong Key Laboratory of Multimedia Trusted Perception and Efficient Computing Xiamen University China

ISBN: (纸本)9798400706868

Generating diverse plausible outputs from a single input is crucial for addressing visual ambiguities, exemplified in medical imaging where experts may provide varying semantic segmentation annotations for the same *** methods handles ambiguous segmentation relying on probabilistic modeling and extensive multi-output annotated data while often struggles with limited ambiguously labeled datasets common in real-world *** surmount the challenge, we propose P2SAM, a novel framework that leverages the Segment Anything Model (SAM)'s prior knowledge for ambiguous object segmentation. By transforming SAM's sensitivity to prompts into an advantage, we introduce a prior probabilistic space for *** results show that P2SAM significantly enhances medical segmentation precision and diversity using minimal ambiguously annotated samples. Benchmarking against state-of-the-art methods demonstrates superior performance with just 5.5% of the training data (+12% Dmax). This approach marks a significant advancement towards deploying probabilistic models in data-limited real-world scenarios. © 2024 ACM.

关键词： Semantic Segmentation

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SAM as the Guide: Mastering Pseudo-Label Refinement in Semi-Supervised Referring Expression Segmentation 41

SAM as the Guide: Mastering Pseudo-Label Refinement in Semi-...

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41st International Conference on Machine Learning, ICML 2024

作者： Yang, Danni Ji, Jiayi Ma, Yiwei Guo, Tianyu Wang, Haowei Sun, Xiaoshuai Ji, Rongrong Key Laboratory of Multimedia Trusted Perception and Efficient Computing Ministry of Education of China School of Informatics Xiamen University 361005 China Youtu Lab Tencent Shanghai China

In this paper, we introduce SemiRES, a semi-supervised framework that effectively leverages a combination of labeled and unlabeled data to perform RES. A significant hurdle in applying semi-supervised techniques to RES is the prevalence of noisy pseudo-labels, particularly at the boundaries of objects. SemiRES incorporates the Segment Anything Model (SAM), renowned for its precise boundary demarcation, to improve the accuracy of these pseudo-labels. Within SemiRES, we offer two alternative matching strategies: IoU-based Optimal Matching (IOM) and Composite Parts Integration (CPI). These strategies are designed to extract the most accurate masks from SAM's output, thus guiding the training of the student model with enhanced precision. In instances where a precise mask cannot be matched from the available candidates, we develop the Pixel-Wise Adjustment (PWA) strategy, guiding the student model's training directly by the pseudo-labels. Extensive experiments on three RES benchmarks-RefCOCO, RefCOCO+, and G-Ref reveal its superior performance compared to fully supervised methods. Remarkably, with only 1% labeled data, our SemiRES outperforms the supervised baseline by a large margin, e.g. +18.64% gains on RefCOCO val set. The project code is available at https://***/nini0919/SemiRES. Copyright 2024 by the author(s)

关键词： Labeled data

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Palm Tree Diseases Detection Using Deep Learning: A Short Review 8

Palm Tree Diseases Detection Using Deep Learning: A Short Re...

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8th IEEE International Conference on Image and Signal Processing and their Applications, ISPA 2024

作者： Bouthaina, Rechachi Rezeg, Khaled Okba, Kazar Merizig, Abdelhak Mohamed Khider University LINFI Laboratory Biskra Algeria Sharjah University Computing and Informatics College Arts Sciences & Information Technology College Kalba United Arab Emirates

ISBN: (纸本)9798350309249

Agriculture is a sector that represents a real indicator of the country's development, and it is a sector that affects national security. The palm tree is a significant, affordable, and nutrient-rich food source that gives millions of people access to wholesome food globally. It can get a lot of illnesses, much like other plants. DL has been effectively implemented in agriculture and has been employed in many other industries. This paper examines the latest advancements in the use of deep learning (DL) for the diagnosis of palm tree diseases. Particularly Convolutional Neural Networks (CNN) are often utilised in agricultural research due to its strong image processing ability. © 2024 IEEE.

关键词： Precision agriculture

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APTO: Accelerating Serialization-Based Point Cloud Transformers with Position-Aware Pruning 25

APTO: Accelerating Serialization-Based Point Cloud Transform...

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30th Asia and South Pacific Design Automation Conference, ASP-DAC 2025

作者： Sun, Qichu Meng, Rui Fan, Haishuang Ding, Fangqiang Lu, Linxi Wu, Jingya Li, Xiaowei Yan, Guihai State Key Laboratory of Processors Institute of Computing Technology Chinese Academy of Sciences University of Chinese Academy of Sciences Beijing China School of Informatics University of Edinburgh Edinburgh United Kingdom State Key Laboratory of Processors Institute of Computing Technology Chinese Academy of Sciences Beijing China State Key Laboratory of Processors Institute of Computing Technology Chinese Academy of Sciences Yusur Technology Co. Ltd. Beijing China

ISBN: (纸本)9798400706356

Point cloud processing has broad applications in autonomous driving and robotics. Serialization-based point cloud transformers map unordered point clouds onto directed curves, use sparse convolution for down-sampling and apply attention in local windows to capture spatial relationships. Despite achieving great accuracy, these models face inference latency challenges: neighbor search in sparse convolution exhibits low parallelism;attention computation remains complex, especially with larger window sizes;softmax introduces data dependencies. This paper proposes APTO, an accelerator for serialization-based models. It uses voxels' z-curve indices to perform neighbor searches in parallel, employs a position-aware pruning strategy using neighboring voxel counts to eliminate useless attention computations, and adopts a fine-grained attention dataflow for parallel processes with minimal data dependencies. Besides, its hardware has dedicated computation cores for efficient processing. Evaluations show that APTO achieves average 10.22×, 3.53× and 2.70× speedups over RTX 4090 GPU, PointAcc, and SpOctA, with 153.59×, 8.57× and 7.25× energy savings. © 2025 Institute of Electrical and Electronics Engineers Inc.. All rights reserved.

关键词： Convolution

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