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AffordAnything is proposed, a training-free method that leverages segmentation cues, motivated by the strong correlation between affordance regions and object subparts, and developed AffordAnything+, a trainable variant that learns to combine these cues.
Inspired by Muon, this work adopts the spectral view for unlearning and proposes Spectral Saliency Unlearning (SSU), a thresholding approach that thresholds weak singular components and updates only those directions supported by a confident unlearning signal.
This work introduces Moving Alphabet, a procedural testbed that renders letters with varying fonts, colors, sizes, and positions, moving in different directions and speeds against a black background, to study how data distribution and caption quality impact text-to-video models.
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