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Random crop-based pseudo-label scoring consistently outperforms SAM-generated masks, revealing a surprising advantage in preserving contextual information for fine-grained classification.
Overcome the scarcity of paired data in speech-preserving facial expression manipulation by personalizing visual-language model prompts with individual visual information and correlating changes in visual and semantic features.
