Papers with Code 아카이브
Human Interaction Recognition
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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- 평가표
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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.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/AVAuco/laeonetplus
공식 코드LAEO-Net++: revisiting people Looking At Each Other in videos
https://github.com/AlbertoSabater/Skeleton-based-One-shot-Action-Recognition
공식 코드One-shot action recognition in challenging therapy scenarios
https://github.com/KAIST-VICLab/SkateFormer
공식 코드SkateFormer: Skeletal-Temporal Transformer for Human Action Recognition
https://github.com/Necolizer/CHASE
공식 코드CHASE: Learning Convex Hull Adaptive Shift for Skeleton-based Multi-Entity Action Recognition
https://github.com/Necolizer/ISTA-Net
공식 코드https://github.com/Walter0807/MotionBERT
공식 코드MotionBERT: A Unified Perspective on Learning Human Motion Representations
https://github.com/arminarj/empathic-grounding
공식 코드https://github.com/ekazakos/auditory-slow-fast
공식 코드https://github.com/gmbition/mgtr
공식 코드https://github.com/kaai520/PGFA
공식 코드Zero-shot Skeleton-based Action Recognition with Prototype-guided Feature Alignment
https://github.com/mauriciolp/inter-rel-net
공식 코드Interaction Relational Network for Mutual Action Recognition
https://github.com/mgiant/2p-gcn
공식 코드Two-person Graph Convolutional Network for Skeleton-based Human Interaction Recognition
https://github.com/mohamedhassanmus/POSA
공식 코드https://github.com/nkliuyifang/me-gcn
공식 코드Learning Mutual Excitation for Hand-to-Hand and Human-to-Human Interaction Recognition
https://github.com/paulchhuang/bstro
공식 코드https://github.com/paulchhuang/rich_toolkit
공식 코드https://github.com/raphaelmemmesheimer/skeleton-dml
공식 코드Skeleton-DML: Deep Metric Learning for Skeleton-Based One-Shot Action Recognition
https://github.com/raphaelmemmesheimer/sl-dml
공식 코드SL-DML: Signal Level Deep Metric Learning for Multimodal One-Shot Action Recognition
https://github.com/sha2nkt/deco
공식 코드DECO: Dense Estimation of 3D Human-Scene Contact In The Wild
https://github.com/zzysteve/skeletonx
공식 코드SkeletonX: Data-Efficient Skeleton-based Action Recognition via Cross-sample Feature Aggregation
https://github.com/CarstenEpic/humos
DECO: Dense Estimation of 3D Human-Scene Contact In The Wild
https://github.com/DJAlexJ/LRCN-for-Video-Regression
Long-term Recurrent Convolutional Networks for Visual Recognition and Description
https://github.com/Deepu1992/VideoClassification
Long-term Recurrent Convolutional Networks for Visual Recognition and Description
https://github.com/LinguoLi/CrosSCLR
NTU RGB+D 120: A Large-Scale Benchmark for 3D Human Activity Understanding
https://github.com/czhaneva/skelemixclr
NTU RGB+D 120: A Large-Scale Benchmark for 3D Human Activity Understanding
https://github.com/doronharitan/human_activity_recognition_LRCN
Long-term Recurrent Convolutional Networks for Visual Recognition and Description
https://github.com/dqj5182/HACO_RELEASE
https://github.com/garythung/torch-lrcn
Long-term Recurrent Convolutional Networks for Visual Recognition and Description
https://github.com/kahnchana/RNN
Long-term Recurrent Convolutional Networks for Visual Recognition and Description
https://github.com/porcelluscavia/audio-model
https://github.com/rlaengud123/CMC_LRCN
Long-term Recurrent Convolutional Networks for Visual Recognition and Description
https://github.com/shahroudy/NTURGB-D
NTU RGB+D 120: A Large-Scale Benchmark for 3D Human Activity Understanding
https://github.com/sujaygarlanka/tennis_stroke_recognition
Long-term Recurrent Convolutional Networks for Visual Recognition and Description
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.