mlsanityData to 7 Oct 2026

Non-local Neural Networks

arXiv 1711.07971 ↗First posted 21 Nov 2017cs.CVcited by 2,290

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Cited by 29 in Diffusion models, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

Both convolutional and recurrent operations are building blocks that process one local neighborhood at a time. In this paper, we present non-local operations as a generic family of building blocks for capturing long-range dependencies. Inspired by the classical non-local means method in computer vision, our non-local operation computes the response at a position as a weighted sum of the features at all positions. This building block can be plugged into many computer vision architectures. On the task of video classification, even without any bells and whistles, our non-local models can compete or outperform current competition winners on both Kinetics and Charades datasets. In static image recognition, our non-local models improve object detection/segmentation and pose estimation on the COCO suite of tasks. Code is available at https://github.com/facebookresearch/video-nonlocal-net .

Key facts. Cited by 2,290 papers in the map (data through 7 Oct 2026). First posted on arXiv 21 Nov 2017.

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Citing papers per year: 2019: 1; 2020: 0; 2021: 805; 2022: 612; 2023: 388; 2024: 224; 2025: 167; 2026: 93.

2,290 papers.

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