CLIP Score
CLIP Score (also written CLIPScore or CLIP-S) is a reference-free automatic evaluation metric that measures how well a text caption matches an image, computed as the rescaled cosine similarity of the image and…
Multimodal AI
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Showing 1-15 of 15 articles
ERQA
ERQA (Embodied Reasoning Question Answering) is a multimodal benchmark released by Google DeepMind in March 2025 to evaluate the embodied reasoning capabilities of vision-language models (VLMs) on robotics…
EgoSchema
EgoSchema is a diagnostic benchmark for evaluating very long-form video language understanding, introduced by Karttikeya Mangalam, Raiymbek Akshulakov, and Jitendra Malik at UC Berkeley.
Fox (benchmark)
Fox is an evaluation suite for fine-grained, multi-page document understanding by large vision-language models.
MM-BrowseComp
MM-BrowseComp is a benchmark for evaluating multimodal web-browsing AI agents, introduced in August 2025 by researchers from ByteDance, Nanjing University, M-A-P, the Institute of Automation of the Chinese…
MMMU
MMMU (Massive Multi-discipline Multimodal Understanding and Reasoning Benchmark) is a multimodal AI benchmark of 11,550 college-level questions that pairs text with images to test expert knowledge and…
MMMU-Pro
MMMU-Pro is a rigorous benchmark for evaluating multimodal AI systems on college-level, expert questions that genuinely require seeing an image, built as a harder and more robust version of the original MMMU…
MMStar
MMStar (Multi-modal Star) is a vision-language model evaluation benchmark consisting of 1,500 multimodal samples that were filtered from six pre-existing benchmarks to ensure both visual dependency (questions…
MathVista
MathVista is a benchmark for evaluating the mathematical reasoning capabilities of foundation models in visual contexts.
Paper2Video
Paper2Video (full title: Paper2Video: Automatic Video Generation from Scientific Papers) is a research project from Show Lab at the National University of Singapore that formalizes and evaluates automatic…
SWE-bench Multimodal
SWE-bench Multimodal (also written SWE-bench M) is a benchmark that measures whether autonomous software-engineering systems can resolve bugs in visual
Video-MME
Video-MME (Video Multi-Modal Evaluation) is a benchmark for testing how well multimodal large language models (MLLMs) understand video, built from 900 manually selected videos totaling 254 hours and 2,700…
Visual Question Answering Models
Visual question answering models are AI systems that take an image and a natural language question about that image and return a natural language answer.
Visual question answering
Visual question answering (VQA) is the task of producing a natural-language answer to a natural-language question about an image.
ZeroBench
ZeroBench is a visual reasoning benchmark built to be effectively impossible for current frontier large multimodal models, which score 0.0% on its main questions.