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

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.

14 pages, IEEE Transactions on Affective Computing

References in corpus (6)

• Two-Stream Convolutional Networks for Action Recognition in Videos
• UCF101: A Dataset of 101 Human Actions Classes From Videos in The Wild
• The Kinetics Human Action Video Dataset
• Multi-Task Zero-Shot Action Recognition with Prioritised Data Augmentation
• A Compare-Aggregate Model for Matching Text Sequences
• Discriminative convolutional Fisher vector network for action recognition

Cited by in corpus (3)

• Action Genome: Actions as Composition of Spatio-temporal Scene Graphs
• Compositional Few-Shot Recognition with Primitive Discovery and Enhancing
• Unifying Few- and Zero-Shot Egocentric Action Recognition