DINO (computer vision)
DINO (self-DIstillation with NO labels) is a family of self-supervised learning methods for computer vision from Meta AI that trains Vision Transformers (ViTs) on unlabeled images and produces general-purpose…
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Showing 1-14 of 14 articles
DINOv2
DINOv2 is a family of self-supervised Vision Transformer models released by Meta AI Research in April 2023 that produces general-purpose visual features transferring to many downstream tasks without…
DINOv3
DINOv3 is a family of self-supervised computer vision foundation models released by Meta AI in August 2025.
Detectron2
Detectron2 is an open-source software library for object detection and image segmentation, built on PyTorch and developed by Facebook AI Research (FAIR), the research group now part of Meta AI.
Ego-Exo4D
Ego-Exo4D is a large-scale, multimodal, multiview video dataset and benchmark suite for computer vision research on skilled human activity, a central resource in egocentric vision.
Ego4D
Ego4D is a large-scale egocentric (first-person) video dataset and benchmark suite for computer vision, assembled by Meta AI (then Facebook AI Research) together with a consortium of 13 universities and labs…
Hiera
Hiera is a hierarchical vision transformer from Meta AI (FAIR), introduced in the paper "Hiera: A Hierarchical Vision Transformer without the Bells-and-Whistles" presented as an oral at the International…
I-JEPA
I-JEPA (Image-based Joint-Embedding Predictive Architecture) is a self-supervised learning method for computer vision developed by Meta AI.
Nougat (model)
Nougat (Neural Optical Understanding for Academic Documents) is a document-understanding model from Meta AI that converts the rendered image of a document page into structured markup text.
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
Project Aria
Project Aria is an egocentric data-collection research program run by Meta's Reality Labs Research. It was announced on September 16, 2020 .
SAM 2
SAM 2 (Segment Anything Model 2) is a promptable visual segmentation model for both images and video developed by Meta AI and released on 29 July 2024.
Sapiens (computer vision)
Sapiens is a family of human-centric computer vision foundation models developed by Meta (Reality Labs), introduced in 2024 and presented as an oral paper at the European Conference on Computer Vision (ECCV)…
Segment Anything Model and Dataset (SAM and SA-1B)
Segment Anything Model (SAM) is a promptable image segmentation foundation model released by Meta AI on April 5, 2023 that lets users "cut out" any object in an image with a single click, box, or mask prompt…