Visual Grounding

Visual grounding studies how systems identify the image regions or objects referred to by language, often producing boxes, points, or masks. Common approaches include aligning text and visual features, selecting among detected objects, using attention or high-resolution crops, and training with localization annotations or reinforcement-learning rewards. The field spans referring-expression benchmarks such as RefCOCO, fine-grained spatial reasoning, medical and remote-sensing images, video, and embodied tasks such as navigation and manipulation. A central challenge is that correct answers do not prove a model used visual evidence: systems still struggle with precise localization, changing scenes, and reliable visual dependence while balancing accuracy, efficiency, and robustness.