visionandlanguage

Vision-and-Language, often written visionandlanguage, is an interdisciplinary field at the intersection of computer vision and natural language processing that studies how to interpret, reason about, and generate language grounded in visual content. The central aim is to connect pixel- and frame-level information with textual representations so that models can understand images and videos through language and produce descriptions, explanations, or answers conditioned on visual input.

Core tasks include image captioning (generated natural language descriptions from images), visual question answering (VQA; answering questions about a scene), and visual grounding (referring expression comprehension; localizing objects referred to by text). Related problems include visual dialogue, image-to-text generation, video captioning, and multimodal retrieval or translation that ties text to visual content.

Prominent datasets span captioning, VQA, and text-in-image tasks, such as COCO Captions, Flickr30k, VQA datasets, VizWiz, and TextCaps. Evaluation commonly uses metrics like BLEU, METEOR, ROUGE, and CIDEr for captions, and accuracy for VQA and grounding tasks, with ongoing work toward more human-aligned and robust measures.

Modeling approaches emphasize multimodal fusion and cross-modal reasoning. Many leading systems use transformers to fuse visual and textual streams via cross-attention, often with large-scale multimodal pretraining (for example joint image-text encoders and CLIP-style models) followed by fine-tuning on downstream VL tasks. Contemporary work also explores multilingual and video inputs, efficiency, and interpretability.

Challenges include achieving robust reasoning beyond surface correlations, handling biased or incomplete data, evaluating grounded language generation, and addressing ethical considerations in deploying multimodal AI.