Diffusion-based Industrial Anomaly Detection
Research on industrial anomaly detection with a focus on diffusion-based reconstruction, semantic conditioning, and robust localization for visual inspection tasks.
Research on industrial anomaly detection with a focus on diffusion-based reconstruction, semantic conditioning, and robust localization for visual inspection tasks.
Research on multimodal fashion image retrieval, with an emphasis on visual-semantic alignment and robust retrieval across image-text modalities.
Exploring multimodal AI for medical understanding, including image-text reasoning, visual grounding, and user-oriented intelligent assistance.
Applying computer vision and language models to analyze product design, packaging, and market strategies in consumer-facing domains.
End-to-end work on intelligent visual applications and personalized customization systems, from algorithm design to system implementation.
Published in CASA 2024, 2024
First-author paper on multimodal fashion image retrieval published at CASA 2024.
Recommended citation: Li, Yingjin. (2024). "Fashion Image Retrieval Based on Multimodal Features Enhancement and Fusion." CASA 2024.
Published:
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Published:
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Undergraduate course, University 1, Department, 2014
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Workshop, University 1, Department, 2015
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