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publications
二次解耦与活体特征渐进式对齐的域自适应人脸反欺诈
Domain Adaptation
This work addresses the cross-domain generalization problem in face anti-spoofing by introducing secondary disentanglement and progressive liveness feature alignment.
AMSNet: Netlist Dataset for AMS Circuits
Dataset
AMSNet builds a large-scale paired dataset of AMS schematics and netlists to support multimodal understanding and automatic design for analog and mixed-signal circuits.
AMSnet 2.0: A Large AMS Database with AI Segmentation for Net Detection
Dataset
AMSnet 2.0 expands the AMS database with AI-based segmentation for net detection and richer positional and digital representations of circuit schematics.
AMSbench: A Comprehensive Benchmark for Evaluating MLLM Capabilities in AMS Circuits
Benchmark
AMSbench is a comprehensive benchmark for evaluating multimodal large language models on AMS circuit perception, analysis, and design tasks.
SHIELD: an evaluation benchmark for face spoofing and forgery detection with multimodal large language models
Benchmark
SHIELD is an evaluation benchmark for multimodal large language models on both face anti-spoofing and face forgery detection tasks.
Symbol and Footprint Database for Electronic Components by Agentic Recognition and Generation
Generation
This work presents an agentic recognition-and-generation pipeline for electronic component symbols and footprints, together with a scalable database for automated PCB design.
AMSnet-KG: A Netlist Dataset for LLM-based AMS Circuit Auto-Design Using Knowledge Graph RAG
Knowledge Graph
AMSnet-KG extends AMS netlist data with a circuit knowledge graph and retrieval-augmented generation to support LLM-based reasoning and auto-design for AMS circuits.
FaceShield: Explainable Face Anti-Spoofing with Multimodal Large Language Models
Explainability
FaceShield proposes an explainable multimodal large language model framework for face anti-spoofing, covering binary classification, attack type recognition, reasoning, and region localization.
UniShield: Unified Face Attack Detection via KG-Informed Multimodal Reasoning
Unified Detection
UniShield introduces a knowledge-grounded multimodal reasoning framework for unified face attack detection across both physical spoofing and digital forgery scenarios.
FAS-R1: A Unified Multi-Task MLLM for Reasoning Face Anti-Spoofing
Reasoning
FAS-R1 is a reasoning-oriented multimodal large language model for unified face anti-spoofing, covering authenticity classification, attack-type recognition, and spoof-region localization.
talks
Talk 1 on Relevant Topic in Your Field
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Conference Proceeding talk 3 on Relevant Topic in Your Field
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teaching
Teaching experience 1
Undergraduate course, University 1, Department, 2014
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Teaching experience 2
Workshop, University 1, Department, 2015
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