Beyond Text Prompts: Precise Concept Erasure through Text-Image Collaboration
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 large-scale training datasets. Existing concept erasure methods, whether text-only or image-assisted, face trade-offs: textual approaches often fail to fully suppress concepts, while naive image-guided methods risk over-erasing unrelated content. We propose TICoE, a text-image Collaborative Erasing framework that achieves precise and faithful concept removal through a continuous convex concept manifold and hierarchical visual representation learning. TICoE precisely removes target concepts while preserving unrelated...
| Authors | Jun Li, Lizhi Xiong, Ziqiang Li et al. |
|---|---|
| Primary category | cs.CV |
| Published | 17 April 2026 |
| Last updated at source | 17 April 2026 |
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