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Learning Action and Reasoning-Centric Image Editing from Videos and Simulations

Neural Information Processing Systems

Object, attribute or stylistic changes can be learned from visually static datasets. On the other hand, high-quality data for action and reasoning-centric edits is scarce and has to come from entirely different sources that cover e.g.



Towards Test-Time Refusals via Concept Negation Peiran Dong 1 Song Guo 2 Junxiao Wang 3 Bingjie Wang

Neural Information Processing Systems

Here is a breakdown of the three steps involved: 1) Prototype: We utilize CLIP to encode a collection of text prompts obtained from social media platforms that express similar negative concepts. These encoded features are then aggregated into a comprehensive prototype feature, capturing the semantics of the negative concepts.