Human Interaction Recognition

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.

Papers

Recently Added Most Hyped Most Active Needs Verification Most Verified

Showing 11–20 of 22 papers

Title	Date	Tasks	Status
Human-to-Human Interaction Detection	Jul 2, 2023	Action DetectionHuman Interaction Recognition	—Unverified
WiFi-TCN: Temporal Convolution for Human Interaction Recognition based on WiFi signal	May 21, 2023	Activity RecognitionHuman Activity Recognition	—Unverified
SkeleTR: Towards Skeleton-based Action Recognition in the Wild	Jan 1, 2023	Action ClassificationAction Detection	—Unverified
Two-person Graph Convolutional Network for Skeleton-based Human Interaction Recognition	Aug 12, 2022	Action ClassificationAction Recognition	CodeCode Available
IGFormer: Interaction Graph Transformer for Skeleton-based Human Interaction Recognition	Jul 25, 2022	Human Interaction Recognition	—Unverified
A Prospective Approach for Human-to-Human Interaction Recognition from Wi-Fi Channel Data using Attention Bidirectional Gated Recurrent Neural Network with GUI Application Implementation	Feb 16, 2022	Activity RecognitionHuman Activity Recognition	—Unverified
Human Interaction Recognition Framework based on Interacting Body Part Attention	Jan 22, 2021	Activity RecognitionActivity Recognition In Videos	—Unverified
Three-Stream Fusion Network for First-Person Interaction Recognition	Feb 19, 2020	Activity RecognitionHuman Interaction Recognition	—Unverified
Interaction Relational Network for Mutual Action Recognition	Oct 11, 2019	Action RecognitionHuman Interaction Recognition	CodeCode Available
Hierarchical Long Short-Term Concurrent Memory for Human Interaction Recognition	Nov 1, 2018	Action RecognitionHuman Interaction Recognition	—Unverified

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All datasets NTU RGB+D 120 NTU RGB+D UT BIT EPIC-SOUNDS SBU SBU / SBU-Refine UT-Interaction

Benchmark Results

#	Model	Metric	Claimed	Verified	Status
1	SkateFormer	Accuracy (Cross-Setup)	93.2	—	Unverified
2	CHASE(CTR-GCN)	Accuracy (Cross-Setup)	92.3	—	Unverified
3	ISTA-Net	Accuracy (Cross-Setup)	91.7	—	Unverified
4	SkeleTR	Accuracy (Cross-Setup)	88.3	—	Unverified
5	IGFormer	Accuracy (Cross-Setup)	86.5	—	Unverified
6	LSTM-IRN	Accuracy (Cross-Setup)	79.6	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	SkateFormer	Accuracy (Cross-Subject)	97.1	—	Unverified
2	CHASE(CTR-GCN)	Accuracy (Cross-Subject)	96.5	—	Unverified
3	SkeleTR	Accuracy (Cross-Subject)	94.9	—	Unverified
4	IGFormer	Accuracy (Cross-Subject)	93.6	—	Unverified
5	LSTM-IRN'fc1inter+intra	Accuracy (Cross-Subject)	90.5	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	H-LSTCM	Accuracy	98.33	—	Unverified
2	Co-LSTSM	Accuracy	95	—	Unverified
3	Raptis et al.	Accuracy	93.3	—	Unverified
4	Donahue et al.	Accuracy	85	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	H-LSTCM	Accuracy	94.03	—	Unverified
2	Co-LSTSM	Accuracy	92.88	—	Unverified
3	Donahue et al.	Accuracy	80.13	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	Slow-Fast(Finetune by Fivewin team)	Top-1 accuracy %	55.11	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	LSTM-IRN'fc1inter+intra	Accuracy	98.2	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	IGFormer	Accuracy	98.4	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	LSTM-IRN'fc1inter+intra	Accuracy (Set 1)	98.3	—	Unverified