https://github.com/nickgkan/3d_diffuser_actor
Act3D: 3D Feature Field Transformers for Multi-Task Robotic Manipulation
Papers with Code 아카이브
Action Detection
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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://github.com/nieci2024/pyglcn
Semi-Supervised Classification with Graph Convolutional Networks
https://github.com/nikitakaraevv/pointnet
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/niveditarahurkar/CS231N-ActionRecognition
UCF101: A Dataset of 101 Human Actions Classes From Videos in The Wild
https://github.com/nnaakkaaii/g2-MLP
Semi-Supervised Classification with Graph Convolutional Networks
https://github.com/noahtren/Graph-Attention-Networks-TensorFlow-2
https://github.com/nuschandra/Tennis-Stroke-Detection
You Only Watch Once: A Unified CNN Architecture for Real-Time Spatiotemporal Action Localization
https://github.com/opeco17/pointnet
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/open-mmlab/mmaction2
https://github.com/open-mmlab/mmflow
FlowNet 2.0: Evolution of Optical Flow Estimation with Deep Networks
https://github.com/open-mmlab/mmskeleton
Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition
https://github.com/pablopardod/dyalyt
https://github.com/parilo/rl-server
https://github.com/paty0504/SIGNTEGRATE
Make Skeleton-based Action Recognition Model Smaller, Faster and Better
https://github.com/pengsl-lab/mssl
https://github.com/pengsl-lab/mssl2drug
https://github.com/peter-yys-yoon/pegcnv2
Richly Activated Graph Convolutional Network for Action Recognition with Incomplete Skeletons
https://github.com/petsol/ContinuousControl_UnityAgent_DDPG_Udacity
https://github.com/philferriere/tfoptflow
FlowNet 2.0: Evolution of Optical Flow Estimation with Deep Networks
https://github.com/piergiaj/latent-subevents
Learning Latent Sub-events in Activity Videos Using Temporal Attention Filters
https://github.com/piergiaj/pytorch-i3d
Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset
https://github.com/pirxus/personalVAD
https://github.com/plnguyen2908/UniTalk-ASD-code
AVA-ActiveSpeaker: An Audio-Visual Dataset for Active Speaker Detection
https://github.com/prajwalgatti/DRL-Collaboration-and-Competition
https://github.com/prajwalgatti/DRL-Continuous-Control
https://github.com/prasadsawant5/PointNet
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/princeton-vl/SimpleView
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/prinshul/GWSDR
Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset
https://github.com/pwc-1/Paper-9/tree/main/3/AVA_cifar
AVA: A Video Dataset of Spatio-temporally Localized Atomic Visual Actions
https://github.com/qema/orca-py
https://github.com/qingzhiwu/Temporal-Context-Aggregation-Network-Pytorch
Temporal Context Aggregation Network for Temporal Action Proposal Refinement
https://github.com/qiwang233/dailydvs-200
HARDVS: Revisiting Human Activity Recognition with Dynamic Vision Sensors
https://github.com/rainyBJ/RTD_RePro
Relaxed Transformer Decoders for Direct Action Proposal Generation
https://github.com/raphaelmemmesheimer/gimme_signals_action_recognition
Gimme Signals: Discriminative signal encoding for multimodal activity recognition
https://github.com/ray-project/ray/tree/master/rllib
https://github.com/raymondyeh07/chirality_nets
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.