Research
Computer Vision
Mask R-CNN
October 22, 2017
Abstract
We present a conceptually simple, flexible, and general framework for object instance segmentation. Our approach efficiently detects objects in an image while simultaneously generating a high-quality segmentation mask for each instance. The method, called Mask R-CNN, extends Faster R-CNN by adding a branch for predicting an object mask in parallel with the existing branch for bounding box recognition. Mask R-CNN is simple to train and adds only a small overhead to Faster R-CNN, running at 5 fps. Moreover, Mask R-CNN is easy to generalize to other tasks, e.g., allowing us to estimate human poses in the same framework. We show top results in all three tracks of the COCO suite of challenges, including instance segmentation, bounding-box object detection, and person keypoint detection. Without tricks, Mask R-CNN outperforms all existing, single-model entries on every task, including the COCO 2016 challenge winners. We hope our simple and effective approach will serve as a solid baseline and help ease future research in instance-level recognition. Code will be made available.
AUTHORS
Research Topics
Related Publications
February 27, 2026
Human & Machine Intelligence
Unified Vision–Language Modeling via Concept Space Alignment
Yifu Qiu, Holger Schwenk, Paul-Ambroise Duquenne
February 27, 2026
February 11, 2026
Computer Vision
UniT: Unified Multimodal Chain-of-Thought Test-time Scaling
Leon Liangyu Chen, Haoyu Ma, Ziqi Huang, Xiaoliang Dai, Jialiang Wang, Zecheng He, Jianwei Yang, Chunyuan Li, Serena Yeung-Levy, Animesh Sinha, Chu Wang, Felix Juefei-Xu, Junzhe Sun, Zhipeng Fan
February 11, 2026
December 18, 2025
Computer Vision
Pixel Seal: Adversarial-only training for invisible image and video watermarking
Alexandre Mourachko, Hady Elsahar, Pierre Fernandez, Sylvestre Rebuffi, Tom Sander, Tomáš Souček, Tuan Tran, Valeriu Lacatusu
December 18, 2025
November 19, 2025
Computer Vision
SAM 3: Segment Anything with Concepts
Ronghang Hu, Peize Sun, Triantafyllos Afouras, Effrosyni Mavroudi, Katherine Xu, Tsung-Han Wu, Yu Zhou, Liliane Momeni, Shuangrui Ding, Sagar Vaze, Francois Porcher, Feng Li, Siyuan Li, Aishwarya Kamath, Ho Kei Cheng, Andrew Huang, Arpit Kalla, Baishan Guo, Chaitanya Ryali, Christoph Feichtenhofer, Didac Suris Coll-Vinent, Haitham Khedr, Jie Lei, Joseph Greer, Kalyan Vasudev Alwala, Kate Saenko, Laura Gustafson, Markus Marks, Meng Wang, Nicolas Carion, Nikhila Ravi, Pengchuan Zhang, Piotr Dollar, Rishi Hazra, Roman Rädle, Shoubhik Debnath, Tengyu Ma, Yuan-Ting Hu
November 19, 2025
June 11, 2019
Computer Vision
ELF OpenGo: An Analysis and Open Reimplementation of AlphaZero | Facebook AI Research
Yuandong Tian, Jerry Ma, Qucheng Gong, Shubho Sengupta, Zhuoyuan Chen, James Pinkerton, Larry Zitnick
June 11, 2019
April 30, 2018
NLP
Computer Vision
Mastering the Dungeon: Grounded Language Learning by Mechanical Turker Descent | Facebook AI Research
Zhilin Yang, Saizheng Zhang, Jack Urbanek, Will Feng, Alexander H. Miller, Arthur Szlam, Douwe Kiela, Jason Weston
April 30, 2018
October 10, 2016
Speech & Audio
Computer Vision
Polysemous Codes | Facebook AI Research
Matthijs Douze, Hervé Jégou, Florent Perronnin
October 10, 2016
June 18, 2018
Speech & Audio
Computer Vision
Low-shot learning with large-scale diffusion | Facebook AI Research
Matthijs Douze, Arthur Szlam, Bharath Hariharan, Hervé Jégou
June 18, 2018
Help Us Pioneer The Future of AI
We share our open source frameworks, tools, libraries, and models for everything from research exploration to large-scale production deployment.
Our approach
Latest news
Foundational models
Meta © 2026