Institution profile

Jiangxi University of Finance and Economics

Academic institutionasia · cn
Official website
Research library60linked papers
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Selected work

Representative Papers

Video Quality Assessment for Online Processing: From Spatial to Temporal Sampling

Dec 01, 2024IEEE transactions on circuits and systems for video technology (Print)

This work addresses online video quality assessment (VQA), targeting the fundamental limits of spatiotemporal redundancy compression to achieve optimal trade-offs between efficiency and accuracy. We propose a joint spatiotemporal sampling strategy that reduces spatial resolution and frame rate to ≤10% of the original while incurring <8% performance degradation. The method comprises a lightweight spatial feature extractor, an efficient temporal fusion module, and a global quality regression network—forming a low-latency, real-time-capable VQA architecture. To our knowledge, this is the first study to systematically characterize the performance tolerance thresholds for spatiotemporal compression in VQA. Extensive experiments on six mainstream public benchmarks demonstrate strong generalization and robustness. Our approach establishes the first practical, high-accuracy, low-overhead solution for real-time VQA in edge-computing and streaming scenarios.

1 citationsRead paper

SANet: Selective Attention Network for Infrared Small Target Detection

Oct 07, 2026

This study addresses the challenge of balancing detection accuracy and false alarm rate in infrared small target detection by proposing a selective attention network. Specifically, a dual-path semantic perception module is designed, integrating standard and pinwheel-shaped convolutions with spatial-channel attention mechanisms to enhance target-background discrimination. Furthermore, an adaptive feature fusion strategy employing spatially variable weights is introduced to overcome the limitations of static skip connections, thereby optimizing multi-scale feature integration. Extensive experiments on three public benchmarks demonstrate that the proposed method achieves up to a 4.32 percentage point improvement in IoU over the second-best approach, while significantly reducing the false alarm rate and increasing the detection probability.

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Forensic-Aware Continual Adaptation for Image Forgery Localization

Sep 29, 2026

This study addresses the limitations of existing image forgery localization methods, which struggle to adapt to emerging manipulation techniques and lack the continual learning capability required to balance old knowledge retention with new domain acquisition over continuous data streams. To this end, this work proposes the first continual learning framework tailored for image forgery localization by establishing the IFL benchmark. It designs spatial mixture-of-forensics-experts and evidence-guided prompting mechanisms to dynamically mine forensic traces, and introduces Fisher-weighted LoRA gradient surgery to mitigate catastrophic forgetting. Experimental results demonstrate that the proposed method achieves state-of-the-art performance in both pixel-level localization and image-level detection across multiple scenarios, enabling effective cross-domain adaptive updates.

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A DSP Framework for ISAC with random signals: From Physical Transceiver to Periodic Models

Sep 26, 2026

This study addresses the ambiguity in the digital signal processing (DSP) chain between physical transceivers and periodic sensing models in communication-centric integrated sensing and communication (ISAC). We construct a unified DSP transceiver framework for single-antenna ISAC that integrates modulation, cyclic prefix insertion, and pulse shaping, thereby establishing a symbol-rate equivalent channel and a periodic matched filtering model. By revealing the DSP mapping mechanism from physical waveforms to the periodic model, the proposed framework achieves linearized communication reception and cyclic-shift equivalence for sensing reception. Numerical experiments validate the superiority of this framework in terms of target range estimation accuracy and bit error rate performance over Rayleigh fading channels under CP-OFDM transmission.

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Recent publications

Latest Papers

SANet: Selective Attention Network for Infrared Small Target Detection

Oct 07, 2026

This study addresses the challenge of balancing detection accuracy and false alarm rate in infrared small target detection by proposing a selective attention network. Specifically, a dual-path semantic perception module is designed, integrating standard and pinwheel-shaped convolutions with spatial-channel attention mechanisms to enhance target-background discrimination. Furthermore, an adaptive feature fusion strategy employing spatially variable weights is introduced to overcome the limitations of static skip connections, thereby optimizing multi-scale feature integration. Extensive experiments on three public benchmarks demonstrate that the proposed method achieves up to a 4.32 percentage point improvement in IoU over the second-best approach, while significantly reducing the false alarm rate and increasing the detection probability.

0 citationsRead paper

Forensic-Aware Continual Adaptation for Image Forgery Localization

Sep 29, 2026

This study addresses the limitations of existing image forgery localization methods, which struggle to adapt to emerging manipulation techniques and lack the continual learning capability required to balance old knowledge retention with new domain acquisition over continuous data streams. To this end, this work proposes the first continual learning framework tailored for image forgery localization by establishing the IFL benchmark. It designs spatial mixture-of-forensics-experts and evidence-guided prompting mechanisms to dynamically mine forensic traces, and introduces Fisher-weighted LoRA gradient surgery to mitigate catastrophic forgetting. Experimental results demonstrate that the proposed method achieves state-of-the-art performance in both pixel-level localization and image-level detection across multiple scenarios, enabling effective cross-domain adaptive updates.

0 citationsRead paper

A DSP Framework for ISAC with random signals: From Physical Transceiver to Periodic Models

Sep 26, 2026

This study addresses the ambiguity in the digital signal processing (DSP) chain between physical transceivers and periodic sensing models in communication-centric integrated sensing and communication (ISAC). We construct a unified DSP transceiver framework for single-antenna ISAC that integrates modulation, cyclic prefix insertion, and pulse shaping, thereby establishing a symbol-rate equivalent channel and a periodic matched filtering model. By revealing the DSP mapping mechanism from physical waveforms to the periodic model, the proposed framework achieves linearized communication reception and cyclic-shift equivalence for sensing reception. Numerical experiments validate the superiority of this framework in terms of target range estimation accuracy and bit error rate performance over Rayleigh fading channels under CP-OFDM transmission.

0 citationsRead paper