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Activity Recognition In Videos
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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/wzzheng/genad
공식 코드https://github.com/wzzheng/gpd
공식 코드https://github.com/xhaughearl/haarmodic
공식 코드Multi-Resolution Haar Network: Enhancing human motion prediction via Haar transform
https://github.com/xiandaguo/drive-mllm
공식 코드https://github.com/xiaogangpeng/socialtgcn
공식 코드The MI-Motion Dataset and Benchmark for 3D Multi-Person Motion Prediction
https://github.com/xincoder/GRIP
공식 코드GRIP++: Enhanced Graph-based Interaction-aware Trajectory Prediction for Autonomous Driving
https://github.com/xrenaa/safety-aware-motion-prediction
공식 코드Safety-aware Motion Prediction with Unseen Vehicles for Autonomous Driving
https://github.com/xushenlz/parksim
공식 코드https://github.com/xxh43/ukmd
공식 코드Unsupervised Kinematic Motion Detection for Part-segmented 3D Shape Collections
https://github.com/y-zheng18/gimo
공식 코드https://github.com/yandex-research/shifts
공식 코드Shifts: A Dataset of Real Distributional Shift Across Multiple Large-Scale Tasks
https://github.com/yf-li15/DisenHCN
공식 코드DisenHCN: Disentangled Hypergraph Convolutional Networks for Spatiotemporal Activity Prediction
https://github.com/yfzhang/vehicle-motion-forecasting
공식 코드https://github.com/youngzhou1999/smartpretrain
공식 코드SmartPretrain: Model-Agnostic and Dataset-Agnostic Representation Learning for Motion Prediction
https://github.com/youshaamurhij/ofmpnet
공식 코드OFMPNet: Deep End-to-End Model for Occupancy and Flow Prediction in Urban Environment
https://github.com/yuhao-nie/stanford-solar-forecasting-dataset
공식 코드https://github.com/yuxiaochen1103/Hi-TRS
공식 코드Hierarchically Self-Supervised Transformer for Human Skeleton Representation Learning
https://github.com/zhangyp15/beverse
공식 코드BEVerse: Unified Perception and Prediction in Birds-Eye-View for Vision-Centric Autonomous Driving
https://github.com/zhaoxing2022/mmn-vsod
공식 코드Motion-aware Memory Network for Fast Video Salient Object Detection
https://github.com/zhejz/hptr
공식 코드Real-Time Motion Prediction via Heterogeneous Polyline Transformer with Relative Pose Encoding
https://github.com/zhejz/trafficbots
공식 코드TrafficBots: Towards World Models for Autonomous Driving Simulation and Motion Prediction
https://github.com/zshn25/pc4consistentdepth
공식 코드Pose Constraints for Consistent Self-supervised Monocular Depth and Ego-motion
https://github.com/zympsyche/bevworld
공식 코드BEVWorld: A Multimodal World Model for Autonomous Driving via Unified BEV Latent Space
https://github.com/2023-MindSpore-1/ms-code-216/tree/main/ReIDStrongBaseline
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/2023-MindSpore-1/ms-code-217/tree/main/tracktor
https://github.com/2023-MindSpore-1/ms-code-217/tree/main/tracktor%2B%2B
https://github.com/2023-MindSpore-1/ms-code-49
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/2023-MindSpore-1/ms-code-7/tree/main/tracktor%2B%2B
https://github.com/2023-MindSpore-4/Code10/tree/main/VGG
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/2023-MindSpore-4/Code6/tree/main/ReIDStrongBaseline
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/2023-MindSpore-4/Code9/tree/main/VGG
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/AIOSYM/dl-model-gallery
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/AbertayMachineLearningGroup/machine-learning-SIEM-water-infrastructure
Improving SIEM for Critical SCADA Water Infrastructures Using Machine Learning
https://github.com/Afsaan/CNN-Architectures
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/AlbertFeng-0405/SceneTransformer-pytorch
Scene Transformer: A unified architecture for predicting multiple agent trajectories
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.