Search posts, papers, and topics
Vision‑RL2 trains a lightweight proposal network via region‑level reinforcement learning to select high‑resolution evidence for multimodal LLMs, allowing coarse‑resolution localization and fine‑resolution recognition. Across six fine‑grained vision benchmarks it reduces visual token count by ~4× while matching or surpassing full‑resolution accuracy.
Latent Interface Training (LIT) first learns a goal‑conditioned action prior without visual input, then adds a pose‑supervised latent interface as the only visual conditioning path. Applied to several vision‑language‑action models, LIT cuts vision‑action shortcuts and lifts LIBERO‑Plus success by 3.9–10.7 points and real‑world task success by 13.3–16.7 points under distribution shifts.
PANORAMA introduces a VLM that grounds each phrase of a caption to pixel masks by selecting from a phrase‑conditioned pool of mask proposals, trained jointly with caption generation. It sets a new benchmark on the PanoCaps dataset and outperforms prior methods on several pixel‑level grounding tasks.
RelateAnything is a 53 M‑parameter model that predicts arbitrary textual relations between any supplied image regions, using a text‑embedding bank instead of a fixed classifier. Trained on a new 4.3 M‑relation dataset, it outperforms prior open‑vocab methods by 2.3‑3.5× mean recall while running in ~20 ms per frame.
