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Beyond Text Prompts: Precise Concept Erasure through Text-Image Collaboration
Jun Li, Lizhi Xiong, Ziqiang Li +4
Text-to-image generative models have achieved impressive fidelity and diversity, but can inadvertently produce unsafe or undesirable content due to implicit biases embedded in larg…
Organizing Unstructured Image Collections using Natural Language
Mingxuan Liu, Zhun Zhong, Jun Li +3
In this work, we introduce and study the novel task of Open-ended Semantic Multiple Clustering (OpenSMC). Given a large, unstructured image collection, the goal is to automatically…
Enhancing Supervised Composed Image Retrieval via Reasoning-Augmented Representation Engineering
Jun Li, Hongjian Dou, Zhenyu Zhang +3
Composed Image Retrieval (CIR) presents a significant challenge as it requires jointly understanding a reference image and a modified textual instruction to find relevant target im…
A Comprehensive Survey on Visual Concept Mining in Text-to-image Diffusion Models
Ziqiang Li, Jun Li, Lizhi Xiong +2
Text-to-image diffusion models have made significant advancements in generating high-quality, diverse images from text prompts. However, the inherent limitations of textual signals…