Learn to cycle: Time-consistent feature discovery for action recognition

06/15/2020
by   Alexandros Stergiou, et al.
0

Temporal motion has been one of the essential components for effectively recognizing actions in videos. Both, time information and features are primarily extracted hierarchically through small sequences of few frames, with the use of 3D convolutions. In this paper, we propose a method that can learn general feature changes across time, making activations unbounded to a temporal locality, by additionally including a general notion of their learned features. Through this recalibration of temporal feature cues across multiple frames, 3D-CNN models are capable of using features that are prevalent over different time segments, while being less constraint by their temporal receptive fields. We present improvements on both high and low capacity models, with the largest benefits being observed in low-memory models, as most of their current drawbacks rely on their poor generalization capabilities because of the low number and feature complexity. We present average improvements, over both corresponding and state-of-the-art models, in the range of 3.67 Kinetics-700 (K-700), 2.75 Clips and Segments (HACS), 3.195

READ FULL TEXT

page 1

page 3

research
04/03/2020

TEA: Temporal Excitation and Aggregation for Action Recognition

Temporal modeling is key for action recognition in videos. It normally c...
research
10/05/2021

Efficient Modelling Across Time of Human Actions and Interactions

This thesis focuses on video understanding for human action and interact...
research
07/16/2020

Challenge report:VIPriors Action Recognition Challenge

This paper is a brief report to our submission to the VIPriors Action Re...
research
12/02/2019

More Is Less: Learning Efficient Video Representations by Big-Little Network and Depthwise Temporal Aggregation

Current state-of-the-art models for video action recognition are mostly ...
research
04/30/2019

Memory-Augmented Temporal Dynamic Learning for Action Recognition

Human actions captured in video sequences contain two crucial factors fo...
research
11/25/2018

Learning Conditional Random Fields with Augmented Observations for Partially Observed Action Recognition

This paper aims at recognizing partially observed human actions in video...

Please sign up or login with your details

Forgot password? Click here to reset