2,338 research outputs found
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 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 .Comment: CVPR 2018, code is available at:
https://github.com/facebookresearch/video-nonlocal-ne
Attention Clusters: Purely Attention Based Local Feature Integration for Video Classification
Recently, substantial research effort has focused on how to apply CNNs or
RNNs to better extract temporal patterns from videos, so as to improve the
accuracy of video classification. In this paper, however, we show that temporal
information, especially longer-term patterns, may not be necessary to achieve
competitive results on common video classification datasets. We investigate the
potential of a purely attention based local feature integration. Accounting for
the characteristics of such features in video classification, we propose a
local feature integration framework based on attention clusters, and introduce
a shifting operation to capture more diverse signals. We carefully analyze and
compare the effect of different attention mechanisms, cluster sizes, and the
use of the shifting operation, and also investigate the combination of
attention clusters for multimodal integration. We demonstrate the effectiveness
of our framework on three real-world video classification datasets. Our model
achieves competitive results across all of these. In particular, on the
large-scale Kinetics dataset, our framework obtains an excellent single model
accuracy of 79.4% in terms of the top-1 and 94.0% in terms of the top-5
accuracy on the validation set. The attention clusters are the backbone of our
winner solution at ActivityNet Kinetics Challenge 2017. Code and models will be
released soon.Comment: The backbone of the winner solution at ActivityNet Kinetics Challenge
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