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Action Detection

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하위 태스크: 8
평가표: 11
보존된 평가 기록: 81

Action Detection aims to find both where and when an action occurs within a video clip and classify what the action is taking place. Typically results are given in the form of action tublets, which are action bounding boxes linked across time in the video. This is related to temporal localization, which seeks to identify the start and end frame of an action, and action recognition, which seeks only to classify which action is taking place and typically assumes a trimmed video.

코드

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

https://bitbucket.org/edge-team-leat/microai_public

공식 코드

Quantization and Deployment of Deep Neural Networks on Microcontrollers

https://bitbucket.org/sahasuman/bmvc2016_code

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Online Spatiotemporal Action Detection and Prediction via Causal Representations

https://github.com/12sakura/cefdet

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Cefdet: Cognitive Effectiveness Network Based on Fuzzy Inference for Action Detection

https://github.com/1xbq1/a2mc

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Attack-Augmentation Mixing-Contrastive Skeletal Representation Learning

https://github.com/1xbq1/psp-learning

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Pyramid Self-attention Polymerization Learning for Semi-supervised Skeleton-based Action Recognition

https://github.com/3dperceptionlab/toad

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Text-driven Online Action Detection

https://github.com/4paradigm-cv/se-stad

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A Simple and Efficient Pipeline to Build an End-to-End Spatial-Temporal Action Detector

https://github.com/AKASH2907/End-to-End-Semi-Supervised-Learning-for-Video-Action-Detection

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End-to-End Semi-Supervised Learning for Video Action Detection

https://github.com/AKASH2907/stable-mean-teacher

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Stable Mean Teacher for Semi-supervised Video Action Detection

https://github.com/ActionR-G/ActionHSIC

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Skeleton-based Action Recognition with Non-linear Dependency Modeling and Hilbert-Schmidt Independence Criterion

https://github.com/AngeloFang/MIPR_UAAN

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Uncertainty-Guided Appearance-Motion Association Network for Out-of-Distribution Action Detection

https://github.com/AnitaEpsilon/HAR

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Human Activity Recognition using Continuous Wavelet Transform and Convolutional Neural Networks

https://github.com/B-HAR-HumanActivityRecognition/B-HAR

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B-HAR: an open-source baseline framework for in depth study of human activity recognition datasets and workflows

https://github.com/BUPTSJZhang/SiT-MLP

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SiT-MLP: A Simple MLP with Point-wise Topology Feature Learning for Skeleton-based Action Recognition

https://github.com/BUTSpeechFIT/AMI_2speaker_subset

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Multi-Speaker and Wide-Band Simulated Conversations as Training Data for End-to-End Neural Diarization

https://github.com/BUTSpeechFIT/EEND_dataprep

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Multi-Speaker and Wide-Band Simulated Conversations as Training Data for End-to-End Neural Diarization

https://github.com/BehnooshParsa/MTL-ERA

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A Multi-Task Learning Approach for Human Activity Segmentation and Ergonomics Risk Assessment

https://github.com/Breezelled/COMODO

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COMODO: Cross-Modal Video-to-IMU Distillation for Efficient Egocentric Human Activity Recognition

https://github.com/CAMMA-public/cholect50

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CholecTriplet2022: Show me a tool and tell me the triplet -- an endoscopic vision challenge for surgical action triplet detection

https://github.com/CAMMA-public/cholectriplet2021

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CholecTriplet2021: A benchmark challenge for surgical action triplet recognition

https://github.com/CDAC-lab/ETFA-Workshop/blob/main/CNN%20and%20LSTM%20for%20Human%20Activity%20Recognition.ipynb

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Human Activity Recognition using Multi-Head CNN followed by LSTM

https://github.com/Chain123/Meta-HAR

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Meta-HAR: Federated Representation Learning for Human Activity Recognition

https://github.com/Chaolei98/FreeZAD

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Training-Free Zero-Shot Temporal Action Detection with Vision-Language Models

https://github.com/Chiaraplizz/ST-TR

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Skeleton-based Action Recognition via Spatial and Temporal Transformer Networks

https://github.com/Claydon-Wang/OFTTA

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Optimization-Free Test-Time Adaptation for Cross-Person Activity Recognition

https://github.com/Computational-Imaging-Lab-IITM/HAR-DVS

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Dynamic Vision Sensors for Human Activity Recognition

https://github.com/DavideBuffelli/TrASenD

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Attention-Based Deep Learning Framework for Human Activity Recognition with User Adaptation

https://github.com/DirtyHarryLYL/HAKE

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HAKE: Human Activity Knowledge Engine

https://github.com/DirtyHarryLYL/HAKE-Action-Torch

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HAKE: Human Activity Knowledge Engine

https://github.com/EHZ9NIWI7/MSF-GZSSAR

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Multi-Semantic Fusion Model for Generalized Zero-Shot Skeleton-Based Action Recognition

https://github.com/Forsad/Stable-Polynomial-Train

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A Training Framework for Optimal and Stable Training of Polynomial Neural Networks

https://github.com/GMouYes/DHC_HGL

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Deep Heterogeneous Contrastive Hyper-Graph Learning for In-the-Wild Context-Aware Human Activity Recognition

https://github.com/Gbouna/MAK-GCN

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Multi-Head Adaptive Graph Convolution Network for Sparse Point Cloud-Based Human Activity Recognition

https://github.com/Gbouna/RE-TCN

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Action Recognition in Real-World Ambient Assisted Living Environment

https://github.com/GeetDsa/Activity_Recognition_3D_CNN

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An IoT Based Framework For Activity Recognition Using Deep Learning Technique

https://github.com/HuCui2022/DSTSA-GCN_Gesture

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DSTSA-GCN: Advancing Skeleton-Based Gesture Recognition with Semantic-Aware Spatio-Temporal Topology Modeling

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