What are the most common benchmarks used for evaluating VLMs?

The most common benchmarks for evaluating Vision-Language Models (VLMs) include Visual Question Answering (VQA), COCO Captions, cross-modal retrieval tasks (e.g., Flickr30k and MS-COCO), and specialized datasets like GQA and OK-VQA. These benchmarks test capabilities such as image-text understanding, caption generation, retrieval accuracy, and complex reasoning. Developers use these standardized evaluations to compare model performance, identify weaknesses, and guide improvements in multimodal AI systems.

One widely used benchmark is VQA v2, which evaluates a model’s ability to answer questions about images. It contains over 1.1 million questions tied to 200,000 real-world images, covering topics like object recognition, spatial relationships, and scene context. Models are scored based on answer accuracy, often using human-generated answers as ground truth. Another key benchmark is COCO Captions, which tests image captioning quality. Models generate descriptive text for images, and metrics like BLEU, METEOR, and CIDEr compare outputs to human-written references. These benchmarks emphasize natural language fluency and alignment with visual content, making them foundational for tasks requiring image-to-text understanding.

Cross-modal retrieval tasks, such as those in Flickr30k and MS-COCO, measure how well models associate images with relevant text (and vice versa). For example, given an image, a model retrieves matching captions from a pool of candidates, scored using recall@k (how often the correct result appears in the top k matches). These benchmarks stress fine-grained alignment between modalities. For advanced reasoning, GQA challenges models with compositional questions requiring logical inference (e.g., “Is the person holding the umbrella wet?”). OK-VQA adds external knowledge requirements (e.g., “What sport is this?” for an image of a cricket bat), forcing models to integrate real-world facts. These benchmarks push VLMs beyond basic recognition into deeper understanding and knowledge application.

By combining these benchmarks, developers gain a comprehensive view of VLM capabilities. Standardized metrics ensure consistent evaluation, while diverse tasks highlight strengths and gaps. For example, a model excelling at VQA might struggle with OK-VQA if lacking external knowledge integration. This multi-faceted approach drives progress in building robust, general-purpose VLMs.