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arXiv Computer Vision By Zesheng Li, Chengchang Pan, Honggang Qi

GraSP-VL: Length as a Semantic Granularity Interface for Vision-Language Representations

Read the original on arXiv Computer Vision →

GraSP‑VL demonstrates that the length of frozen vision‑language embeddings can serve as a controllable semantic interface. By learning a shared near‑orthogonal prefix transform, the method creates a Semantic Matryoshka where short prefixes encode coarse semantics and longer prefixes reveal finer language‑grounded distinctions, all while preserving the original embedding geometry. Experiments on COCO/Flickr30K and SugarCrepe‑clean show strong performance with negligible drift in the full embedding space.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computer Vision.

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