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The cause of redetachment after vitrectomy with air tamponade for a cohort of 1715 patients with retinal detachment:an analysis of retinal breaks reopening

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Eye and Vision 2025年第1期11卷 33-40页

作者： Chuandi Zhou Chufeng Gu Bo Li Yujie Wang Yanan Hu Xinping She Ya Shi Mingming Ma Tao Sun Qinghua Qiu Ying Fan Fenge Chen Hong Wang Kun Liu Xiaodong Sun Xun Xu Zhi Zheng Department of Ophthalmology Shanghai General HospitalShanghai Jiao Tong University School of MedicineNo.100 Haining RoadHongkou DistrictShanghaiChina National Clinical Research Center for Eye Diseases No.100 Haining RoadHongkou DistrictShanghaiChina Shanghai Key Laboratory of Ocular Fundus Diseases No.100 Haining RoadHongkou DistrictShanghaiChina Shanghai Engineering Center for Visual Science and Photomedicine No.100 Haining RoadHongkou DistrictShanghaiChina Shanghai Engineering Center for Precise Diagnosis and Treatment of Eye Diseases No.100 Haining RoadHongkou DistrictShanghaiChina

Background To investigate the prevalence and predictors of retinal breaks reopening after vitrectomy with air tamponade in rhegmatogenous retinal detachment(RRD).Methods A retrospective cohort study was conducted in Shanghai General *** review was performed among 1715 patients with primary RRD who received pars plana vitrectomy(PPV)with air tamponade as initial *** were followed up for *** clinical features of the eyes with retinal breaks reopening were *** regression was constructed to investigate the predictors for breaks *** A total of 137(7.99%)patients had recurrent retinal detachment after PPV with air *** causes of surgery failure included new or missed retinal breaks(48.9%),reopening of original tears(43.8%)and proliferative vitreoretinopathy(7.3%).The median time to recurrence for the patients with breaks reopening was 18.0 *** logistic regression indicated that the presence of retinal break(s)≥1.5 disc diameters(DD)(odds ratio[OR]:2.68,95%confdence interval[CI]:11.04–6.92,P=0.041),and shorter period for restricted activities(OR:0.94,95%CI:0.89–0.99,P=0.020)were the independent predictors for breaks *** Breaks reopening is an important cause for retinal redetachment after PPV with air tamponade in primary *** frst 2–4 weeks after surgery is the“risk period”for breaks *** attention should be paid for patients with retinal break(s)≥1.5 DD.A prolonged period for restricted activities is recommended.

关键词： Retinal redetachment Air tamponade Vitrectomy Retinal breaks reopening Postoperative restricted activities

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Resource-Efficient Affordance Grounding with Complementary Depth and Semantic Prompts

arXiv

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arXiv 2025年

作者： Huang, Yizhou Yang, Fan Zhu, Guoliang Li, Gen Shi, Hao Zuo, Yukun Chen, Wenrui Li, Zhiyong Yang, Kailun School of Robotics and the National Engineering Research Center of Robot Visual Perception and Control Technology Hunan University China School of Informatics University of Edinburgh United Kingdom State Key Laboratory of Extreme Photonics and Instrumentation Zhejiang University China

Affordance refers to the functional properties that an agent perceives and utilizes from its environment, and is key perceptual information required for robots to perform actions. This information is rich and multimodal in nature. Existing multimodal affordance methods face limitations in extracting useful information, mainly due to simple structural designs, basic fusion methods, and large model parameters, making it difficult to meet the performance requirements for practical deployment. To address these issues, this paper proposes the BiT-Align image-depth-text affordance mapping framework. The framework includes a Bypass Prompt Module (BPM) and a Text Feature Guidance (TFG) attention selection mechanism. BPM integrates the auxiliary modality depth image directly as a prompt to the primary modality RGB image, embedding it into the primary modality encoder without introducing additional encoders. This reduces the model’s parameter count and effectively improves functional region localization accuracy. The TFG mechanism guides the selection and enhancement of attention heads in the image encoder using textual features, improving the understanding of affordance characteristics. Experimental results demonstrate that the proposed method achieves significant performance improvements on public AGD20K and HICO-IIF datasets. On the AGD20K dataset, compared with the current state-of-the-art method, we achieve a 6.0% improvement in the KLD metric, while reducing model parameters by 88.8%, demonstrating practical application values. The source code will be made publicly available at https://***/DAWDSE/BiT-Align. Copyright © 2025, The Authors. All rights reserved.

关键词： Image coding

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Exploring inter- and intra-modal relations in compositional zero-shot learning

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Neurocomputing 2025年 639卷

作者： Zhang, Xiao Chen, Hui Jing, Haodong Ma, Yongqiang Zheng, Nanning National Key Laboratory of Human–Machine Hybrid Augmented Intelligence National Engineering Research Center for Visual Information and Applications and Institute of Artificial Intelligence and Robotics Xi'an Jiaotong University Shaanxi Xi'an710049 China

Compositional Zero-Shot Learning (CZSL) aims to recognize unknown compositions by leveraging learned concepts of states and objects. Prior methods have typically emphasized either inter-modal relation for multi-modal fusion, ignoring the entanglement within state–object pairs, or solely intra-modal relation for enhancing representations, neglecting the association between vision and language domains. To tackle these limitations, we propose a CZSL framework that simultaneously learns inter- and intra-modal relations to improve image-label alignment. Firstly, we explore inter-modal relation to enable image features to grasp the cross-modal information from states and objects. The image–text fusion method facilitates the modeling of text-aware image features and image-aware text features, improving the model's compositional recognition capability. Secondly, due to the contextuality within state–object pairs, we further explore intra-modal relation to exploit semantic information from various representation subspaces, facilitating the comprehensive semantic expression of text features. Moreover, we propose a composition fusion module to establish semantic entanglement within state–object compositions. Extensive experiments demonstrate that our method significantly surpasses the state-of-the-art methods in both closed-world and open-world settings. © 2025 Elsevier B.V.

关键词： Zero-shot learning

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visual-Linguistic Feature Alignment with Semantic and Kinematic Guidance for Referring Multi-Object Tracking

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IEEE Transactions on Multimedia 2025年

作者： Li, Yizhe Zhou, Sanping Qin, Zheng Wang, Le National Key Laboratory of Human-Machine Hybrid Augmented Intelligence National Engineering Research Center for Visual Information and Applications Institute of Artificial Intelligence and Robotics Xi'an Jiaotong University Shaanxi Xi'an710049 China

Referring Multi-Object Tracking (RMOT) aims to dynamically track an arbitrary number of referred targets in a video sequence according to the language expression. Previous methods mainly focus on cross-modal fusion at the feature level with designed structures. However, the insufficient visual-linguistic alignment is prone to causing visual-linguistic mismatches, leading to some targets being tracked but not correctly referred especially when facing the language expression with complex semantics or motion descriptions. To this end, we propose to conduct visual-linguistic alignment with semantic and kinematic guidance to effectively align the visual features with more diverse language expressions. In this paper, we put forward a novel end-to-end RMOT framework SKTrack, which follows the transformer-based architecture with a Language-Guided Decoder (LGD) and a Motion-Aware Aggregator (MAA). In particular, the LGD performs deep semantic interaction layer-by-layer in a single frame to enhance the alignment ability of the model, while the MAA conducts temporal feature fusion and alignment across multiple frames to enable the alignment between visual targets and language expression with motion descriptions. Extensive experiments on the Refer-KITTI and Refer-KITTI-v2 demonstrate that SKTrack achieves state-of-the-art performance and verify the effectiveness of our framework and its components. © 1999-2012 IEEE.

关键词： Semantics

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Refiner: Fine-grained Cross-modal Concepts Refinement for Compositional Zero-Shot Learning

Refiner: Fine-grained Cross-modal Concepts Refinement for Co...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Xiao Zhang Haodong Jing Hui Chen Yongqiang Ma Nanning Zheng National Key Laboratory of Human-Machine Hybrid Augmented Intelligence National Engineering Research Center for Visual Information and Applications and Institute of Artificial Intelligence and Robotics Xi’an Jiaotong University China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Recent Compositional Zero-Shot Learning (CZSL) methods increasingly adopt the pre-trained vision-language models to capture the contextual relations between image and text spaces. However, the single-class-token design from Transformer-based encoder inevitably captures contextual information from unrelated objects and background, thus hindering the modeling of fine-grained class-specific visual features. Suffering from cross-modal gap, prior methods also struggle to improve compositional recognition performance. To address these issues, we propose a fine-grained cross-modal concepts refinement framework, termed as Refiner, which comprises two pivotal components: (i) the fine-grained concepts refinement of image embeddings to capture state-object context within visual scenes, and (ii) the cross-modal information fusion to mitigate the modality gap. By leveraging learnable query vectors to capture region-specific semantic information pertinent to composition labels, our approach refines visual representations with fine-grained state-object context information. As for cross-modal information fusion, we construct a robust image-to-text mapping by aligning visual embeddings with states, objects, and compositions, respectively. Extensive experiments demonstrate that our Refiner achieves new state-of-the-art performance across all popular benchmarks in both closed- and open-world settings.

关键词： visualization Zero shot learning Semantics Benchmark testing Signal processing Transformers Vectors Acoustics Speech processing Context modeling

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Compressed Domain Prior-Guided Video Super-Resolution for Cloud Gaming Content

arXiv

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arXiv 2025年

作者： Wang, Qizhe Yin, Qian Huang, Zhimeng Jiang, Weijia Su, Yi Ma, Siwei Zhang, Jiaqi National Engineering Research Center of Visual Technology School of Computer Science Peking University China Migu Interactive Entertainment Co. Ltd. China

Cloud gaming is an advanced form of Internet service that necessitates local terminals to decode within limited resources and time latency. Super-Resolution (SR) techniques are often employed on these terminals as an efficient way to reduce the required bit-rate bandwidth for cloud gaming. However, insufficient attention has been paid to SR of compressed game video content. Most SR networks amplify block artifacts and ringing effects in decoded frames while ignoring edge details of game content, leading to unsatisfactory reconstruction results. In this paper, we propose a novel lightweight network called Coding Prior-Guided Super-Resolution (CPGSR) to address the SR challenges in compressed game video content. First, we design a Compressed Domain Guided Block (CDGB) to extract features of different depths from coding priors, which are subsequently integrated with features from the U-net backbone. Then, a series of re-parameterization blocks are utilized for reconstruction. Ultimately, inspired by the quantization in video coding, we propose a partitioned focal frequency loss to effectively guide the model’s focus on preserving high-frequency information. Extensive experiments demonstrate the advancement of our approach. Copyright © 2025, The Authors. All rights reserved.

关键词： Network coding

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TS-CGNet: Temporal-Spatial Fusion Meets centerline-Guided Diffusion for BEV Mapping

arXiv

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arXiv 2025年

作者： Hong, Xinying Li, Siyu Zeng, Kang Shi, Hao Peng, Bomin Yang, Kailun Li, Zhiyong The College of Computer Science and Electronic Engineering Hunan University Changsha410082 China The School of Robotics The National Engineering Research Center of Robot Visual Perception and Control Technology Hunan University Changsha410082 China The State Key Laboratory of Extreme Photonics and Instrumentation Zhejiang University Hangzhou310027 China

Bird’s Eye View (BEV) perception technology is crucial for autonomous driving, as it generates top-down 2D maps for environment perception, navigation, and decision-making. Nevertheless, the majority of current BEV map generation studies focusing on visual map generation lack depth-aware reasoning capabilities. They exhibit limited efficacy in managing occlusions and handling complex environments, with a notable decline in perceptual performance under adverse weather conditions or low-light scenarios. Therefore, this paper proposes TS-CGNet, which leverages Temporal-Spatial fusion with centerline-Guided diffusion. This visual framework, grounded in prior knowledge, is designed for integration into any existing network for building BEV maps. Specifically, this framework is decoupled into three parts: Local mapping system involves the initial generation of semantic maps using purely visual information;The Temporal-Spatial Aligner Module (TSAM) integrates historical information into mapping generation by applying transformation matrices;The centerline-Guided Diffusion Model (CGDM) is a prediction module based on the diffusion model. CGDM incorporates centerline information through spatial-attention mechanisms to enhance semantic segmentation reconstruction. We construct BEV semantic segmentation maps by our methods on the public nuScenes and the robustness benchmarks under various corruptions. Our method improves 1.90%, 1.73%, and 2.87% for perceived ranges of 60×30m, 120×60m, and 240×60m in the task of BEV HD mapping. TS-CGNet attains an improvement of 1.92% for perceived ranges of 100×100m in the task of BEV semantic mapping. Moreover, TS-CGNet achieves an average improvement of 2.92% in detection accuracy under varying weather conditions and sensor interferences in the perception range of 240×60m. The source code will be publicly available at https://***/krabs-H/TS-CGNet. Copyright © 2025, The Authors. All rights reserved.

关键词： Decision making

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Semantic Graph Embedded Energy Minimization Learning for Scene Graph Generation

Semantic Graph Embedded Energy Minimization Learning for Sce...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Jinghang Chen Chi Zhang Yuehu Liu Le Wang Institute of Artificial Intelligence and Robotics Xi’an Jiaotong University Xi’an China National Engineering Research Center for Visual Information and Applications National Key Laboratory of Human-Machine Hybrid Augmented Intelligence

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

The performance of current scene graph generation models is affected by training with cross-entropy loss, exacerbating the problem of prediction bias stemming from biased training data. Energy-based model adopts a learning method for joint image and scene graph to alleviate this challenge. However, this method only focuses on the visual features of images, neglecting the rich relation information contained in the semantic space. To address this issue, we innovatively employ powerful pre-trained large models to realize a simple yet effective semantic graph embedded energy minimization framework for the SGG task. Specifically, we use large models to generate image descriptions and extract relation triplets, which are then transformed into semantic graphs with entities as nodes and relations as edges. Moreover, by mapping these graphs into the same space using GNN to learn the minimal energy value, our approach enables SGG model to learn structural information in both semantic and visual spaces. We validate the effectiveness and efficiency of our method on the SGG benchmark visual Genome dataset. Compared with prevailing models and EBM, we achieve a significant performance improvement of up to 2.99% and 2.24%, respectively.

关键词： Training Learning systems visualization Image edge detection Semantics Training data Signal processing Predictive models Minimization Speech processing

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CT-UIO: Continuous-Time UWB-Inertial-Odometer Localization Using Non-Uniform B-spline with Fewer Anchors

arXiv

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arXiv 2025年

作者： Sun, Jian Sun, Wei Zhang, Genwei Yang, Kailun Li, Song Meng, Xiangqi Deng, Na Tan, Chongbin National Engineering Research Center of Robot Visual Perception and Control Technology Hunan University Changsha410012 China College of Electrical and Information Engineering Hunan University China School of Robotics Hunan University Changsha410012 China State Key Laboratory of NBC Protection for Civilian Beijing102205 China

Ultra-wideband (UWB) based positioning with fewer anchors has attracted significant research interest in recent years, especially under energy-constrained conditions. However, most existing methods rely on discrete-time representations and smoothness priors to infer a robot’s motion states, which often struggle with ensuring multi-sensor data synchronization. In this paper, we present an efficient UWB-Inertial-odometer localization system, utilizing a non-uniform B-spline framework with fewer anchors. Unlike traditional uniform B-spline-based continuous-time methods, we introduce an adaptive knot-span adjustment strategy for non-uniform continuous-time trajectory representation. This is accomplished by adjusting control points dynamically based on movement speed. To enable efficient fusion of IMU and odometer data, we propose an improved Extended Kalman Filter (EKF) with innovation-based adaptive estimation to provide short-term accurate motion prior. Furthermore, to address the challenge of achieving a fully observable UWB localization system under few-anchor conditions, the Virtual Anchor (VA) generation method based on multiple hypotheses is proposed. At the backend, we propose a CT-UIO factor graph with an adaptive sliding window for global trajectory estimation. Comprehensive experiments conducted on corridor and exhibition hall datasets validate the proposed system’s high precision and robust performance. The codebase and datasets of this work will be open-sourced at https://***/JasonSun623/CT-UIO. Copyright © 2025, The Authors. All rights reserved.

关键词： Continuous time systems

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MULSAM: Multidimensional Attention with Hardware Acceleration for Efficient Intrusion Detection on Vehicular CAN Bus

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IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems 2025年

作者： Xu, He Shi, Xiaokang Liu, Hansheng Wang, Yanwen Lu, Jiwu Zeng, Haibo Li, Renfa Wu, Di Hunan University National Engineering Research Center for Robot Visual Perception and Control Technology Hunan Changsha410082 China Hunan University College of Electrical and Information Engineering Hunan Changsha410082 China Hunan University Shenzhen Research Institute Shenzhen518000 China Virginia Tech Department of Electrical and Computer Engineering BlacksburgVA24061 United States Hunan University College of Computer Science and Electronic Engineering Hunan Changsha410082 China

Controller Area Network (CAN) protocol is an efficient standard enabling communication among Electronic Control Units (ECUs). However, the CAN bus is vulnerable to malicious attacks because of a lack of defense features. In this article, a novel vehicle Intrusion Detection System (IDS) is developed. The challenge is that existing techniques of Intrusion Detection Systems (IDS) rarely consider attacks with small-batch, which are characterized by their small attack scale and concealed attack patterns, posing a significant threat to driving safety. To solve this problem, we developed an algorithm model that merges Multi-dimensional Long Short-Term Memory (MD-LSTM) and Self-Attention Mechanism (SAM), shortly named MULSAM. The MULSAM model was compared with other baseline models, including Stacked LSTM, MD-LSTM, etc. Experiments show that our approach has the best detection accuracy (98.98%) and training stability. Further, to speed up the inference of MULSAM on edge, the hardware accelerator is implemented on FPGA devices using technologies such as parallelization, modular, pipeline, and fixed-point quantization. Experiments show that our FPGA-based acceleration scheme has a better energy efficiency than the CPU platform. Even with a certain degree of quantification, the acceleration model for MULSAM still displays a high detection accuracy of 98.81% and a low latency of 1.88 ms. © 1982-2012 IEEE.

关键词： Intrusion detection

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