Vision-Language Grounding
Overview
Vision-language grounding studies how models connect language to specific visual evidence, enabling them to locate a described object, segment a region, track an entity through video, or act on an instruction. Common approaches align text with image regions using contrastive learning, detection, segmentation, and point or box prediction; newer multimodal models also use attention, structured scene representations, visual search, and step-by-step evidence grounding. Evaluation spans referring-expression benchmarks, high-resolution and video localization, document understanding, and embodied tasks such as navigation and robotic manipulation. A central challenge is that correct answers do not prove models used the image: language priors, ambiguity, small or changing targets, and long-horizon context can all undermine faithful and robust grounding.
Latest papers
420 papers49 papers in the last four weeks, down 6% on the four weeks before. 0.3% of all new papers.