Papers › Motion Feature Network: Fixed Motion Filter for Action Recognition

Motion Feature Network: Fixed Motion Filter for Action Recognition

26 Jul 2018ECCV 2018 9arXiv:1807.10037archive 2025-07-28

Myunggi Lee, Seungeui Lee, Sungjoon Son, Gyu-tae Park, Nojun Kwak

Spatio-temporal representations in frame sequences play an important role in the task of action recognition. Previously, a method of using optical flow as a temporal information in combination with a set of RGB images that contain spatial information has shown great performance enhancement in the action recognition tasks. However, it has an expensive computational cost and requires two-stream (RGB and optical flow) framework. In this paper, we propose MFNet (Motion Feature Network) containing motion blocks which make it possible to encode spatio-temporal information between adjacent frames in a unified network that can be trained end-to-end. The motion block can be attached to any existing CNN-based action recognition frameworks with only a small additional cost. We evaluated our network on two of the action recognition datasets (Jester and Something-Something) and achieved competitive performances for both datasets by training the networks from scratch.

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Tasks

Action RecognitionAction Recognition In VideosOptical Flow EstimationTemporal Action Localization

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Action Recognition Something-Something V1 Motion Feature Net Top 1 Accuracy 43.9 #70 of 74 Archive leaderboard report
Action Recognition In Videos Jester (Gesture Recognition) MFNet Val 96.68 #3 of 9 Archive leaderboard report
Action Recognition In Videos Something-Something V1 Motion Feature Net Top 1 Accuracy 43.9 #2 of 3 Archive leaderboard report

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