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SSRN 2025年

作者： Yu, Hong Diao, Yujie Liu, Xueyang Wang, Hexin Zhu, Jialin Gao, Jie Tianjin Central Hospital of Gynecology Obstetrics Tianjin300100 China College of Intelligence and Computing Tianjin University Tianjin300350 China Tianjin Key Laboratory of Cognitive Computing and Application Tianjin300350 China Tianjin Key Laboratory of Advanced Networking Tianjin300350 China Department of Diagnostic and Therapeutic Ultrasonography Tianjin Medical University Cancer Institute and Hospital Tianjin300060 China

Whole Slide Imaging (WSI) analysis, pivotal in digital pathology, faces significant challenges due to its ultra-high resolution (often exceeding 10k * 10k pixels) and the scarcity of pixel-level annotations. While Multiple Instance Learning (MIL) mitigates these issues by leveraging image-level labels, existing methods relying on Top-K strategies for pseudo-label generation only annotate a subset of high-confidence instances, leaving the majority without supervision and limiting classifier optimization. To address this critical gap, we propose SimPseudo, a novel similarity comparison-based instance pseudo-label generator that adaptively assigns high-quality soft pseudo-labels to all instances in WSI bags. Our framework integrates three key innovations: (1) A Top-N1 similarity strategy that combines MIL classifier confidence with pre-trained negative prototypes to assign soft pseudo-labels to low-confidence instances through feature similarity comparisons;(2) A negative example prototype generation mechanism that generates pseudo-labels for negative instances, further enhancing the model's ability to handle diverse example types;(3) A pseudo-label correction module that refines soft labels via classifier-derived probabilities, enabling error suppression and one-hot encoding. SimPseudo is designed to be model-agnostic, making it compatible with diverse MIL classifiers. Extensive experiments on Camelyon16 and Camelyon17 demonstrate that SimPseudo brings consistent improvements across various evaluation metrics, outperforming existing Top-K baselines. The framework provides precise instance-level localization of pathological regions and establishes a new paradigm for comprehensive instance supervision in WSI analysis without requiring additional annotations. © 2025, The Authors. All rights reserved.

关键词： Pixels

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Replace2Self: Self-Supervised Denoising based on Voxel Replacing and Image Mixing for Diffusion MRI

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IEEE Transactions on Medical Imaging 2025年 PP卷 PP页

作者： Wu, Linhai Wang, Lihui Deng, Zeyu Zhu, Yuemin Wei, Hongjiang Guizhou University Key Laboratory of Advanced Medical Imaging and Intelligent Computing of Guizhou Province Engineering Research Center of Text Computing & Cognitive Intelligence Ministry of Education State Key Laboratory of Public Big Data College of Computer Science and Technology 550025 China Univ Lyon INSA Lyon CNRS Inserm CREATIS UMR 5220 LyonF-69621 France Shanghai Jiao Tong University School of Biomedical Engineering Shanghai200030 China

Low signal to noise ratio (SNR) remains one of the limitations of diffusion weighted (DW) imaging. How to suppress the influence of noise on the subsequent analysis about the tissue microstructure is still challenging. This work proposed a novel self-supervised learning model, Replace2Self, to effectively reduce spatial correlated noise in DW images. Specifically, a voxel replacement strategy based on similar block matching in Q-space was proposed to destroy the correlations of noise in DW image along one diffusion gradient direction. To alleviate the signal gap caused by the voxel replacement, an image mixing strategy based on complementary mask was designed to generate two different noisy DW images. After that, these two noisy DW images were taken as input, and the non-correlated noisy DWimage after voxel replacement was taken as learning target, a denoising network was trained for denoising. To promote the denoising performance, a complementary mask mixing consistency loss and an inverse replacement regularization loss were also proposed. Through the comparisons against several existing DW image denoising methods on extensive simulation data with different noise distributions, noise levels and b-values, as well as the acquisition datasets and the ablation experiments, we verified the effectiveness of the proposed method. Regardless of the noise distribution and noise level, the proposed method achieved the highest PSNR, which was at least 1.9% higher than the suboptimal method when the noise level reaches 10%. Furthermore, our method has superior generalization ability due to the use of the proposed strategies. © 2025 IEEE. All rights reserved.

关键词： Signal to noise ratio

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A Hierarchical Semantic Distillation Framework for Open-Vocabulary Object Detection

arXiv

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arXiv 2025年

作者： Fu, Shenghao Yan, Junkai Yang, Qize Wei, Xihan Xie, Xiaohua Zheng, Wei-Shi School of Computer Science and Engineering Sun Yat-sen University Guangzhou510006 China Peng Cheng Laboratory China Tongyi Lab Alibaba Group China School of Computer Science and Engineering Sun Yat-sen University Guangdong Guangzhou China Guangdong Key Laboratory of Information Security Technology Sun Yat-sen University Guangdong Guangzhou China Key Laboratory of Machine Intelligence and Advanced Computing Sun Yat-sen University Ministry of Education Guangdong Guangzhou China

Open-vocabulary object detection (OVD) aims to detect objects beyond the training annotations, where detectors are usually aligned to a pre-trained vision-language model, e.g., CLIP, to inherit its generalizable recognition ability so that detectors can recognize new or novel objects. However, previous works directly align the feature space with CLIP and fail to learn the semantic knowledge effectively. In this work, we propose a hierarchical semantic distillation framework named HD-OVD to construct a comprehensive distillation process, which exploits generalizable knowledge from the CLIP model in three aspects. In the first hierarchy of HD-OVD, the detector learns fine-grained instance-wise semantics from the CLIP image encoder by modeling relations among single objects in the visual space. Besides, we introduce text space novel-class-aware classification to help the detector assimilate the highly generalizable classwise semantics from the CLIP text encoder, representing the second hierarchy. Lastly, abundant image-wise semantics containing multi-object and their contexts are also distilled by an image-wise contrastive distillation. Benefiting from the elaborated semantic distillation in triple hierarchies, our HD-OVD inherits generalizable recognition ability from CLIP in instance, class, and image levels. Thus, we boost the novel AP on the OV-COCO dataset to 46.4% with a ResNet50 backbone, which outperforms others by a clear margin. We also conduct extensive ablation studies to analyze how each component works. Code is available at https://***/iSEE-laboratory/HD-OVD. Copyright © 2025, The Authors. All rights reserved.

关键词： Semantics

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A novel deep high-level concept-mining jointing hashing model for unsupervised cross-modal retrieval

High-Confidence Computing

引用

High-Confidence computing 2025年第2期5卷

作者： Dong, Chun-Ru Zhang, Jun-Yan Zhang, Feng Hua, Qiang Xu, Dachuan Hebei Key Laboratory of Machine Learning and Computational Intelligence College of Mathematics and Information Science Hebei University Baoding071002 China Beijing Institute for Scientific and Engineering Computing Beijing University of Technology Beijing100124 China

Unsupervised cross-modal hashing has achieved great success in various information retrieval applications owing to its efficient storage usage and fast retrieval speed. Recent studies have primarily focused on training the hash-encoded networks by calculating a sample-based similarity matrix to improve the retrieval performance. However, there are two issues remain to solve: (1) The current sample-based similarity matrix only considers the similarity between image-text pairs, ignoring the different information densities of each modality, which may introduce additional noise and fail to mine key information for retrieval;(2) Most existing unsupervised cross-modal hashing methods only consider alignment between different modalities, while ignoring consistency between each modality, resulting in semantic conflicts. To tackle these challenges, a novel Deep High-level Concept-mining Jointing Hashing (DHCJH) model for unsupervised cross-modal retrieval is proposed in this study. DHCJH is able to capture the essential high-level semantic information from image modalities and integrate into the text modalities to improve the accuracy of guidance information. Additionally, a new hashing loss with a regularization term is introduced to avoid the cross-modal semantic collision and false positive pairs problems. To validate the proposed method, extensive comparison experiments on benchmark datasets are conducted. Experimental findings reveal that DHCJH achieves superior performance in both accuracy and efficiency. The code of DHCJH is available at Github. © 2024 The Author(s)

关键词： Online searching

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Vision-Language Model IP Protection via Prompt-based Learning

arXiv

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arXiv 2025年

作者： Wang, Lianyu Wang, Meng Fu, Huazhu Zhang, Daoqiang The Key Laboratory of Brain-Machine Intelligence Technology Ministry of Education China Centre for Innovation and Precision Eye Health Yong Loo Lin School of Medicine National University of Singapore Singapore Institute of High Performance Computing Agency for Science Technology and Research Singapore

Vision-language models (VLMs) like CLIP (Contrastive Language-Image Pre-Training) have seen remarkable success in visual recognition, highlighting the increasing need to safeguard the intellectual property (IP) of well-trained models. Effective IP protection extends beyond ensuring authorized usage;it also necessitates restricting model deployment to authorized data domains, particularly when the model is fine-tuned for specific target domains. However, current IP protection methods often rely solely on the visual backbone, which may lack sufficient semantic richness. To bridge this gap, we introduce IP-CLIP, a lightweight IP protection strategy tailored to CLIP, employing a prompt-based learning approach. By leveraging the frozen visual backbone of CLIP, we extract both image style and content information, incorporating them into the learning of IP prompt. This strategy acts as a robust barrier, effectively preventing the unauthorized transfer of features from authorized domains to unauthorized ones. Additionally, we propose a style-enhancement branch that constructs feature banks for both authorized and unauthorized domains. This branch integrates self-enhanced and cross-domain features, further strengthening IP-CLIP’s capability to block features from unauthorized domains. Finally, we present new three metrics designed to better balance the performance degradation of authorized and unauthorized domains. Comprehensive experiments in various scenarios demonstrate its promising potential for application in IP protection tasks for VLMs. © 2025, CC BY-NC-ND.

关键词： Semantics

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Hypergraph-Enhanced Multi-Granularity Stochastic Weight Completion in Sparse Road Networks

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ACM Transactions on Knowledge Discovery from Data 2025年第3期19卷

作者： Han, Xiaolin Zhang, Yikun Ma, Chenhao Shang, Xuequn Cheng, Reynold Grubenmann, Tobias Li, Xiaodong Northwestern Polytechnical Universit China and Laboratory for Advanced Computing and Intelligence Engineering Wuxi Xi'an China Northwestern Polytechnical University Xi'an China The Chinese University of Hong Kong - Shenzhen Shenzhen China The University of Hong Kong Hong Kong Edinburgh Napier University Edinburgh United Kingdom Xiamen University China and Key Laboratory of Multimedia Trusted Perception and Efficient Computing Ministry of Education of China Xiamen China

Road network applications, such as navigation, incident detection, and Point-of-Interest (POI) recommendation, make extensive use of network edge weights (e.g., traveling times). Some of these weights can be missing, especially in a road network where traffic data may not be available for every road. In this article, we study the stochastic weight completion (SWC) problem, which computes the weight distributions of missing road edges. This is difficult, due to the intricate temporal and spatial correlations among neighboring edges. Besides, the road network can be sparse, i.e., there is a lack of traveling information in a large portion of the network. To tackle these challenges, we propose a multi-granularity framework for Region-Wise Graph Completion (RegGC). To learn coarse spatial correlations among distantly located roads, we construct a region-wise hypergraph neural architecture based on semantic region dependencies. For finer spatial correlations, we incorporate contextual road network properties (e.g., speed limits, lane counts, and road types). Moreover, it incorporates recent and periodic dimensions of road traffic. We evaluate RegGC against 10 existing methods on 3 real road network datasets. They show that RegGC is more effective and efficient than state-of-the-art solutions. © 2025 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Semantic Segmentation

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Heterogeneous Dynamic-Aware GNN for RUL Prediction of Aero-engine

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IEEE Transactions on Instrumentation and Measurement 2025年 74卷

作者： Jin, Yang Meng, Zhengguo Hu, Qinghua Chen, Dongyue Civil Aviation University of China Tianjin Key Laboratory for Civil Aircraft Airworthiness and Maintenance Tianjin 300300 China Civil Aviation University of China College of Aeronautical Engineering Tianjin 300300 China Tianjin University College of Intelligence and Computing Tianjin Key Laboratory of Machine Learning Tianjin 300350 China

Accurate Remaining Useful Life (RUL) prediction is crucial for achieving predictive maintenance. However, the performance degradation process of equipment is not only manifested as anomalies in individual variables but also as changes in the constraint relationships among multiple variables. Human operations and changes in operating conditions further complicate the state representation of the performance degradation process, making RUL prediction for complex systems like aero-engines a challenging task. This paper proposes a novel graph flow learning model for RUL prediction in such complex systems with dynamic interactive operations, termed the Heterogeneous Dynamic-Aware Graph Neural Network (HDA-GNN). Firstly, various correlation evaluation metrics are utilized to convert sensor signals into heterogeneous graphs to represent the complex relationships among sensor signals. Then, Gated Recurrent Unit (GRU) and attention mechanisms are employed to dynamically calculate the edge weights of the heterogeneous graphs, and Graph Convolutional Network (GCN) is used to integrate the features of these dynamic heterogeneous graphs. Finally, the Transformer is applied to learn the temporal features within the graph flows. This model is capable of learning dynamic features under human external operation disturbances from multiple perspectives, thereby reducing the impact of randomness of human operations on dynamic features. Experimental results on the NASA C-MAPSS dataset demonstrate the effectiveness and superiority of this model for RUL prediction tasks in complex systems like aero-engines. © 1963-2012 IEEE.

关键词： Graph Flow Learning Graph Neural Network RUL Prediction Spatiotemporal Features Transformer

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Progressive Human Motion Generation Based on Text and Few Motion Frames

arXiv

引用

arXiv 2025年

作者： Zeng, Ling-An Wu, Gaojie Wu, Ancong Hu, Jian-Fang Zheng, Wei-Shi School of Artificial Intelligence Sun Yat-sen University Guangdong Zhuhai519082 China School of Computer Science and Engineering Sun Yat-sen University Guangzhou510275 China School of Computer Science and Engineering Sun Yat-sen University Guangdong Guangzhou510275 China Guangdong Key Laboratory of Information Security Technology Sun Yat-sen University Guangdong Guangzhou510275 China Key Laboratory of Machine Intelligence and Advanced Computing Sun Yat-sen University Ministry of Education Guangdong Guangzhou510275 China

Although existing text-to-motion (T2M) methods can produce realistic human motion from text description, it is still difficult to align the generated motion with the desired postures since using text alone is insufficient for precisely describing diverse postures. To achieve more controllable generation, an intuitive way is to allow the user to input a few motion frames describing precise desired postures. Thus, we explore a new Text-Frame-to-Motion (TF2M) generation task that aims to generate motions from text and very few given frames. Intuitively, the closer a frame is to a given frame, the lower the uncertainty of this frame is when conditioned on this given frame. Hence, we propose a novel Progressive Motion Generation (PMG) method to progressively generate a motion from the frames with low uncertainty to those with high uncertainty in multiple stages. During each stage, new frames are generated by a Text-Frame Guided Generator conditioned on frame-aware semantics of the text, given frames, and frames generated in previous stages. Additionally, to alleviate the train-test gap caused by multi-stage accumulation of incorrectly generated frames during testing, we propose a Pseudo-frame Replacement Strategy for training. Experimental results show that our PMG outperforms existing T2M generation methods by a large margin with even one given frame, validating the effectiveness of our PMG. Code will be released. Copyright © 2025, The Authors. All rights reserved.

关键词：

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TRNet: Two-Tier Recursion Network for Co-Salient Object Detection

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IEEE Transactions on Circuits and Systems for Video Technology 2025年

作者： Cong, Runmin Yang, Ning Liu, Hongyu Zhang, Dingwen Huang, Qingming Kwong, Sam Zhang, Wei Shandong University School of Control Science and Engineering Jinan250061 China Ministry of Education Key Laboratory of Machine Intelligence and System Control Jinan250061 China Beijing Jiaotong University Institute of Information Science Beijing100044 China Beijing Key Laboratory of Advanced Information Science and Network Technology Beijing100044 China Northwestern Polytechnical University Brain and Artificial Intelligence Laboratory School of Automation Xi'an710129 China University of Chinese Academy of Sciences School of Computer Science and Technology Beijing101408 China Lingnan University Hong Kong

Co-salient object detection (CoSOD) is to find the salient and recurring objects from a series of relevant images, where modeling inter-image relationships plays a crucial role. Different from the commonly used direct learning structure that inputs all the intra-image features into some well-designed modules to represent the inter-image relationship, we resort to adopting a recursive structure for inter-image modeling, and propose a two-tier recursion network (TRNet) to achieve CoSOD in this paper. The two-tier recursive structure of the proposed TRNet is embodied in two stages of inter-image extraction and distribution. On the one hand, considering the task adaptability and inter-image correlation, we design an inter-image exploration with recursive reinforcement module to learn the local and global inter-image correspondences, guaranteeing the validity and discriminativeness of the information in the step-by-step propagation. On the other hand, we design a dynamic recursion distribution module to fully exploit the role of inter-image correspondences in a recursive structure, adaptively assigning common attributes to each individual image through an improved semi-dynamic convolution. Experimental results on five prevailing CoSOD benchmarks demonstrate that our TRNet outperforms other competitors in terms of various evaluation metrics. The code and results of our method are available at https://***/rmcong/TRNetTCSVT2025. © 1991-2012 IEEE.

关键词： HTTP

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High Efficiency Wiener Filter-based Point Cloud Quality Enhancement for MPEG G-PCC

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IEEE Transactions on Circuits and Systems for Video Technology 2025年

作者： Wei, Yuxuan Wang, Zehan Guo, Tian Liu, Hao Shen, Liquan Yuan, Hui Shandong University School of Control Science and Engineering Jinan250061 China Ministry of Education Key Laboratory of Machine Intelligence and System Control Jinan250061 China Yantai University School of Computer and Control Engineering Yantai264005 China Shanghai University Shanghai Institute for Advanced Communication and Data Science Shanghai200072 China

Point clouds, which directly record the geometry and attributes of scenes or objects by a large number of points, are widely used in various applications such as virtual reality and immersive communication. However, due to the huge data volume and unstructured geometry, efficient compression of point clouds is very crucial. The Moving Picture Expert Group is establishing a geometry-based point cloud compression (G-PCC) standard for both static and dynamic point clouds in recent years. Although lossy compression of G-PCC can achieve a very high compression ratio, the reconstruction quality is relatively low, especially at low bitrates. To mitigate this problem, we propose a high efficiency Wiener filter that can be integrated into the encoder and decoder pipeline of G-PCC to improve the reconstruction quality as well as the rate-distortion performance for dynamic point clouds. Specifically, we first propose a basic Wiener filter, and then improve it by introducing coefficients inheritance and variance-based point classification for the Luma component. Besides, to reduce the complexity of the nearest neighbor search during the application of the Wiener filter, we also propose a Morton code-based fast nearest neighbor search algorithm for efficient calculation of filter coefficients. Experimental results demonstrate that the proposed method can achieve average Bjøntegaard delta rates of -6.1%, -7.3%, and -8.0% for Luma, Chroma Cb, and Chroma Cr components, respectively, under the condition of lossless-geometry-lossy-attributes configuration compared to the latest G-PCC encoding platform (i.e., geometry-based solid content test model version 7.0 release candidate 2) by consuming affordable computational complexity. © 2025 IEEE. All rights reserved.

关键词： Wiener filtering

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