Selected work

Representative Papers

HVPNet: A Bio-Inspired Network for General Salient and Camouflaged Object Detection

Jun 30, 2026

Existing multimodal salient and camouflaged object detection methods suffer from structural complexity and large parameter counts, making it challenging to balance accuracy and efficiency. Inspired by the human visual system, this work proposes a lightweight, unified architecture that integrates a Retinal Integration Module (RIM) for hierarchical, multi-stage cross-modal feature fusion and a Cortical Decoder (CD) that mimics visual cortical mechanisms for layered decoding. This approach establishes a biologically inspired, simplified modeling paradigm capable of supporting diverse modalities and tasks within a single framework. Evaluated across four modalities, seven tasks, and 22 datasets, the model achieves an excellent trade-off between accuracy and efficiency with a compact structure, demonstrating strong generalization capability.

0 citationsRead paper

MathMistake Checker: A Comprehensive Demonstration for Step-by-Step Math Problem Mistake Finding by Prompt-Guided LLMs

Mar 06, 2025

This study addresses the challenge of automated fine-grained error localization in lengthy mathematical reasoning processes. Methodologically, it introduces a two-stage LLM-based diagnostic paradigm: first, prompt-guided chain-of-thought reasoning identifies logical errors at each step; second, cross-modal error attribution integrates formula visual recognition (CV) with multi-step consistency verification. The system supports reference-free open-ended evaluation and generates interpretable feedback. Key contributions include the first prompt-driven stepwise diagnostic framework, a reference-answer-free open scoring mechanism, and a vision–language collaborative error attribution model. Evaluated on computational and word problems, the system achieves 92.7% accuracy in erroneous-step identification—outperforming baselines by 31.4 percentage points. It has been deployed in intelligent tutoring platforms across three secondary schools.

0 citationsRead paper

Recent publications

Latest Papers

HVPNet: A Bio-Inspired Network for General Salient and Camouflaged Object Detection

Jun 30, 2026

Existing multimodal salient and camouflaged object detection methods suffer from structural complexity and large parameter counts, making it challenging to balance accuracy and efficiency. Inspired by the human visual system, this work proposes a lightweight, unified architecture that integrates a Retinal Integration Module (RIM) for hierarchical, multi-stage cross-modal feature fusion and a Cortical Decoder (CD) that mimics visual cortical mechanisms for layered decoding. This approach establishes a biologically inspired, simplified modeling paradigm capable of supporting diverse modalities and tasks within a single framework. Evaluated across four modalities, seven tasks, and 22 datasets, the model achieves an excellent trade-off between accuracy and efficiency with a compact structure, demonstrating strong generalization capability.

0 citationsRead paper

MathMistake Checker: A Comprehensive Demonstration for Step-by-Step Math Problem Mistake Finding by Prompt-Guided LLMs

Mar 06, 2025

This study addresses the challenge of automated fine-grained error localization in lengthy mathematical reasoning processes. Methodologically, it introduces a two-stage LLM-based diagnostic paradigm: first, prompt-guided chain-of-thought reasoning identifies logical errors at each step; second, cross-modal error attribution integrates formula visual recognition (CV) with multi-step consistency verification. The system supports reference-free open-ended evaluation and generates interpretable feedback. Key contributions include the first prompt-driven stepwise diagnostic framework, a reference-answer-free open scoring mechanism, and a vision–language collaborative error attribution model. Evaluated on computational and word problems, the system achieves 92.7% accuracy in erroneous-step identification—outperforming baselines by 31.4 percentage points. It has been deployed in intelligent tutoring platforms across three secondary schools.

0 citationsRead paper