TARN: Temporal Attentive Relation Network for Few-Shot and Zero-Shot Action Recognition

TL;DR

TARN is a novel neural network that uses temporal attention and meta-learning to improve few-shot and zero-shot action recognition by comparing variable-length video representations without fine-tuning.

Contribution

Introduces a temporal attentive relation network with attention mechanisms and deep-distance learning for improved few-shot and zero-shot action recognition.

Findings

Outperforms state-of-the-art in few-shot action recognition

Achieves competitive results in zero-shot action recognition

Does not require fine-tuning or additional memory representations

Abstract

In this paper we propose a novel Temporal Attentive Relation Network (TARN) for the problems of few-shot and zero-shot action recognition. At the heart of our network is a meta-learning approach that learns to compare representations of variable temporal length, that is, either two videos of different length (in the case of few-shot action recognition) or a video and a semantic representation such as word vector (in the case of zero-shot action recognition). By contrast to other works in few-shot and zero-shot action recognition, we a) utilise attention mechanisms so as to perform temporal alignment, and b) learn a deep-distance measure on the aligned representations at video segment level. We adopt an episode-based training scheme and train our network in an end-to-end manner. The proposed method does not require any fine-tuning in the target domain or maintaining additional representations as is the case of memory networks. Experimental results show that the proposed architecture outperforms the state of the art in few-shot action recognition, and achieves competitive results in zero-shot action recognition.