Non-local Neural Networks | RDL Research Database

Abstract

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 .

Keywords

Computer scienceArtificial neural networkArtificial intelligence

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Publication Info

Year: 2018
Type: preprint
Pages: 7794-7803
Citations: 10740
Access: Closed

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APA Style

                            
                                    Xiaolong Wang, 
                                
                                    Ross Girshick, 
                                
                                    Abhinav Gupta
                                
                                et al.
                            
                            (2018). 
                            Non-local Neural Networks. 
                            2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition
                            
                            , 7794-7803.
                            https://doi.org/10.1109/cvpr.2018.00813

Identifiers

DOI: 10.1109/cvpr.2018.00813
arXiv: 1711.07971

Data Quality

Data completeness: 84%