Human Interaction Recognition

4 papers with code • 7 benchmarks • 7 datasets

Human Interaction Recognition (HIR) is a field of study that involves the development of computer algorithms to detect and recognize human interactions in videos, images, or other multimedia content. The goal of HIR is to automatically identify and analyze the social interactions between people, their body language, and facial expressions.

Most implemented papers

Slow-Fast Auditory Streams For Audio Recognition

ekazakos/auditory-slow-fast 5 Mar 2021

We propose a two-stream convolutional network for audio recognition, that operates on time-frequency spectrogram inputs.

Interaction Relational Network for Mutual Action Recognition

mauriciolp/inter-rel-net 11 Oct 2019

Our solution is able to achieve state-of-the-art performance on the traditional interaction recognition datasets SBU and UT, and also on the mutual actions from the large-scale dataset NTU RGB+D.

Two-person Graph Convolutional Network for Skeleton-based Human Interaction Recognition

mgiant/2p-gcn 12 Aug 2022

To overcome the above shortcoming, we introduce a novel unified two-person graph to represent inter-body and intra-body correlations between joints.

Interactive Spatiotemporal Token Attention Network for Skeleton-based General Interactive Action Recognition

Necolizer/ISTA-Net 14 Jul 2023

To address these problems, we propose an Interactive Spatiotemporal Token Attention Network (ISTA-Net), which simultaneously model spatial, temporal, and interactive relations.