Non-local Neural Networks

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 [4] in computer...

Celý popis

Uložené v:
Podrobná bibliografia
Vydané v:2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition s. 7794 - 7803
Hlavní autori: Wang, Xiaolong, Girshick, Ross, Gupta, Abhinav, He, Kaiming
Médium: Konferenčný príspevok..
Jazyk:English
Vydavateľské údaje: IEEE 01.06.2018
Predmet:
Computational modeling
Computer vision
Convolution
Convolutional codes
Feedforward systems
Neural networks
Three-dimensional displays
ISSN:1063-6919
On-line prístup:Získať plný text
Tagy: Pridať tag
Žiadne tagy, Buďte prvý, kto otaguje tento záznam!
Popis
Shrnutí: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 [4] 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 nonlocal 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 will be made available.
ISSN:1063-6919
DOI:10.1109/CVPR.2018.00813