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Activity Recognition

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하위 태스크: 11
평가표: 4
보존된 평가 기록: 10

Human Activity Recognition is the problem of identifying events performed by humans given a video input. It is formulated as a binary (or multiclass) classification problem of outputting activity class labels. Activity Recognition is an important problem with many societal applications including smart surveillance, video search/retrieval, intelligent robots, and other monitoring systems.

Source: Learning Latent Sub-events in Activity Videos Using Temporal Attention Filters

코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/Mvrjustid/IMWUT-Hierarchical-HAR-PBD

공식 코드

Leveraging Activity Recognition to Enable Protective Behavior Detection in Continuous Data

https://github.com/NIEQiang001/unsupervised-human-pose

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Unsupervised 3D Human Pose Representation with Viewpoint and Pose Disentanglement

https://github.com/NVlabs/conv-tt-lstm

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Convolutional Tensor-Train LSTM for Spatio-temporal Learning

https://github.com/Nachwa/object_states

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Recognizing Manipulation Actions from State-Transformations

https://github.com/Necolizer/CHASE

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CHASE: Learning Convex Hull Adaptive Shift for Skeleton-based Multi-Entity Action Recognition

https://github.com/Necolizer/ISTA-Net

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

https://github.com/NyleSiddiqui/MultiView_Actions

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DVANet: Disentangling View and Action Features for Multi-View Action Recognition

https://github.com/OpenGVLab/VideoMAEv2

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VideoMAE V2: Scaling Video Masked Autoencoders with Dual Masking

https://github.com/OrdinaryQin/TCA-GCN

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Skeleton-based Action Recognition via Temporal-Channel Aggregation

https://github.com/Oshana/ESWA

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A Comparison of Deep Learning and Established Methods for Calf Behaviour Monitoring

https://github.com/OxWearables/ssl-wearables

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Self-supervised Learning for Human Activity Recognition Using 700,000 Person-days of Wearable Data

https://github.com/PIC4SeRCentre/MPOSE2021_Dataset

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Action Transformer: A Self-Attention Model for Short-Time Pose-Based Human Action Recognition

https://github.com/PaddlePaddle/models/tree/develop/PaddleCV/video/models/bmn

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BMN: Boundary-Matching Network for Temporal Action Proposal Generation

https://github.com/PanasonicConnect/ego4d_lta_ens_srw_seqdep_verb_noun_cooc

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Technical Report for Ego4D Long Term Action Anticipation Challenge 2023

https://github.com/ParitoshParmar/Fitness-AQA

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Domain Knowledge-Informed Self-Supervised Representations for Workout Form Assessment

https://github.com/ParitoshParmar/HalluciNet

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HalluciNet-ing Spatiotemporal Representations Using a 2D-CNN

https://github.com/ParitoshParmar/MTL-AQA

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What and How Well You Performed? A Multitask Learning Approach to Action Quality Assessment

https://github.com/ParitoshParmar/Win-Fail-Action-Recognition

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Win-Fail Action Recognition

https://github.com/PeihaoChen/RSPNet

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RSPNet: Relative Speed Perception for Unsupervised Video Representation Learning

https://github.com/PeizhuoLi/neural-blend-shapes

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Learning Skeletal Articulations with Neural Blend Shapes

https://github.com/PetarV-/GAT

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Graph Attention Networks

https://github.com/PingYu-iris/SA-GCN

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Structure-Aware Human-Action Generation

https://github.com/PinzhengWang322/Reveal_Attention_Drift

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Revealing and Mitigating Over-Attention in Knowledge Editing

https://github.com/R00Kie-Liu/Sampler

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Task-adaptive Spatial-Temporal Video Sampler for Few-shot Action Recognition

https://github.com/R00Kie-Liu/TA2N

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TA2N: Two-Stage Action Alignment Network for Few-shot Action Recognition

https://github.com/RBurchard/hd-epic-tap

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Enhancing Wearable Tap Water Audio Detection through Subclass Annotation in the HD-Epic Dataset

https://github.com/RS2002/KNN-MMD

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KNN-MMD: Cross Domain Wireless Sensing via Local Distribution Alignment

https://github.com/Raminghorbanii/Self-Supervised-PPG-Representation-Learning-Shows-High-Inter-Subject-Variability

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Self-Supervised PPG Representation Learning Shows High Inter-Subject Variability

https://github.com/RanaMostafaAbdElMohsen/Human_Activity_Recognition_using_Wearable_Sensors_Review_Challenges_Evaluation_Benchmark

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Human Activity Recognition using Wearable Sensors: Review, Challenges, Evaluation Benchmark

https://github.com/Rxannro/SWL-Adapt

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SWL-Adapt: An Unsupervised Domain Adaptation Model with Sample Weight Learning for Cross-User Wearable Human Activity Recognition

https://github.com/SJTUHaiyangYu/BackdoorMBTI

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BackdoorMBTI: A Backdoor Learning Multimodal Benchmark Tool Kit for Backdoor Defense Evaluation

https://github.com/SPEECHCOG/data2vec_maiju

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Evaluation of self-supervised pre-training for automatic infant movement classification using wearable movement sensors

https://github.com/SUTDCV/Animal-Kingdom

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Animal Kingdom: A Large and Diverse Dataset for Animal Behavior Understanding

https://github.com/SUTDCV/UAV-Human

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UAV-Human: A Large Benchmark for Human Behavior Understanding with Unmanned Aerial Vehicles

https://github.com/Sausage-SONG/Few-shot-action-recognition

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Semi-Supervised Few-Shot Atomic Action Recognition

https://github.com/SeanChen0220/PoseFall

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Video Based Fall Detection Using Human Poses

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.