Papers with Code 아카이브
Action Recognition
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Action Recognition is a computer vision task that involves recognizing human actions in videos or images. The goal is to classify and categorize the actions being performed in the video or image into a predefined set of action classes.
In the video domain, it is an open question whether training an action classification network on a sufficiently large dataset, will give a similar boost in performance when applied to a different temporal task or dataset. The challenges of building video datasets has meant that most popular benchmarks for action recognition are small, having on the order of 10k videos.
Please note some benchmarks may be located in the Action Classification or Video Classification tasks, e.g. Kinetics-400.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/holistic-video-understanding/HVU-Dataset
공식 코드https://github.com/holzbock/st_mlp
공식 코드A Spatio-Temporal Multilayer Perceptron for Gesture Recognition
https://github.com/hongguangzhang/mlso-tmm-master
공식 코드https://github.com/hongyan1123/skeletonmae
공식 코드SkeletonMAE: Graph-based Masked Autoencoder for Skeleton Sequence Pre-training
https://github.com/hotfinda/videomambapro
공식 코드https://github.com/houshd/MLSTM-FCN
공식 코드https://github.com/howardzh01/ppma
공식 코드Learning Human Action Recognition Representations Without Real Humans
https://github.com/hrishavbakulbarua/darkbert
공식 코드MD-BERT: Action Recognition in Dark Videos via Dynamic Multi-Stream Fusion and Temporal Modeling
https://github.com/htwang14/PA-HMDB51
공식 코드Privacy-Preserving Deep Action Recognition: An Adversarial Learning Framework and A New Dataset
https://github.com/huangxiusheng/dem
공식 코드https://github.com/huiguanlab/hico
공식 코드Hierarchical Contrast for Unsupervised Skeleton-based Action Representation Learning
https://github.com/hulianyuyy/STGAT
공식 코드Spatial Temporal Graph Attention Network for Skeleton-Based Action Recognition
https://github.com/hustyyq/where-to-edit
공식 코드https://github.com/hyeon-jo/PSPNet
공식 코드Self-Supervised Visual Learning by Variable Playback Speeds Prediction of a Video
https://github.com/iai-hrc/ha-vid
공식 코드HA-ViD: A Human Assembly Video Dataset for Comprehensive Assembly Knowledge Understanding
https://github.com/ibm/sifar-pytorch
공식 코드https://github.com/icidsl/harflow3d
공식 코드HARFLOW3D: A Latency-Oriented 3D-CNN Accelerator Toolflow for HAR on FPGA Devices
https://github.com/ideal-idea/sap
공식 코드https://github.com/idt-iti/mmfusion-iml
공식 코드MMFusion: Combining Image Forensic Filters for Visual Manipulation Detection and Localization
https://github.com/iduta/iresnet
공식 코드https://github.com/iduta/pyconv
공식 코드Pyramidal Convolution: Rethinking Convolutional Neural Networks for Visual Recognition
https://github.com/iesymiao/cd-gcn
공식 코드A Central Difference Graph Convolutional Operator for Skeleton-Based Action Recognition
https://github.com/iit-pavis/uhar_skeletal_laplacian
공식 코드https://github.com/imj2185/STAR
공식 코드https://github.com/inowlzy/smart
공식 코드SMART: Scene-motion-aware human action recognition framework for mental disorder group
https://github.com/inria-larsen/activity-recognition-prediction-wearable
공식 코드A Variational Time Series Feature Extractor for Action Prediction
https://github.com/intellabs/multimodal_cognitive_ai
공식 코드Analyzing Zero-Shot Abilities of Vision-Language Models on Video Understanding Tasks
https://github.com/intelligolabs/sparsity_sc_ee
공식 코드Enhancing Split Computing and Early Exit Applications through Predefined Sparsity
https://github.com/interpretml/gam-changer
공식 코드Interpretability, Then What? Editing Machine Learning Models to Reflect Human Knowledge and Values
https://github.com/interpretml/interpret
공식 코드Interpretability, Then What? Editing Machine Learning Models to Reflect Human Knowledge and Values
https://github.com/iskwak/DetetctingActionStarts
공식 코드https://github.com/ispamm/josenet
공식 코드JOSENet: A Joint Stream Embedding Network for Violence Detection in Surveillance Videos
https://github.com/iurada/talos-task-arithmetic
공식 코드Efficient Model Editing with Task-Localized Sparse Fine-tuning
https://github.com/j-h-hwang/temporal_shuffling
공식 코드Temporal Shuffling for Defending Deep Action Recognition Models against Adversarial Attacks
https://github.com/jacklishufan/mamba-nd
공식 코드Mamba-ND: Selective State Space Modeling for Multi-Dimensional Data
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.