Agentic video understanding
Agentic video understanding is an approach to video analysis in which a reasoning model chooses which parts of a video to inspect for a particular question.
Computer Vision
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Showing 1-18 of 18 articles
CLIP (Contrastive Language-Image Pre-training)
CLIP, short for Contrastive Language-Image Pre-training, is a family of neural networks developed by OpenAI researchers to learn a shared representation of images and natural-language descriptions.
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…
Computer-use agent
A computer-use agent (CUA) is a category of AI agent in artificial intelligence that performs tasks by directly operating a general-purpose computer's graphical user interface (GUI) the way a human does, by…
DeepSeek-OCR
DeepSeek-OCR is an open-source optical character recognition (OCR) and document-understanding system released by DeepSeek on 20 October 2025 that pioneers a contexts optical compression paradigm: it encodes…
Donut (Model)
Donut (Document understanding transformer) is an OCR-free visual document understanding model introduced by researchers at NAVER CLOVA in the paper "OCR-free Document Understanding Transformer," first posted…
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.
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…
Perception Encoder
Perception Encoder (PE) is a family of vision and vision-language encoders from Meta AI's Fundamental AI Research (FAIR) group, released in April 2025
Robostral Navigate
Robostral Navigate is an 8-billion-parameter vision language model developed by Mistral AI for instruction-following robot navigation.
SigLIP
SigLIP (Sigmoid Loss for Language-Image Pre-training) is a family of vision-language encoders developed by researchers at Google DeepMind that pre-trains image and text encoders by treating each image-text…
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…
Vision Encoder
A vision encoder is the neural network component that turns an image into a sequence of numerical vectors that other models can consume.
Vision language model
Vision-language models (VLMs) are artificial intelligence models that learn relationships between visual data and natural language.
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.