https://github.com/inowlzy/smart
공식 코드SMART: Scene-motion-aware human action recognition framework for mental disorder group
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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
SMART: Scene-motion-aware human action recognition framework for mental disorder group
A Variational Time Series Feature Extractor for Action Prediction
InTraGen: Trajectory-controlled Video Generation for Object Interactions
Fast Template Matching and Update for Video Object Tracking and Segmentation
Inst-IT: Boosting Multimodal Instance Understanding via Explicit Visual Prompt Instruction Tuning
Text-Visual Prompting for Efficient 2D Temporal Video Grounding
VideoSAGE: Video Summarization with Graph Representation Learning
ICSVR: Investigating Compositional and Syntactic Understanding in Video Retrieval Models
Event-Based Video Frame Interpolation With Cross-Modal Asymmetric Bidirectional Motion Fields
Interpretability, Then What? Editing Machine Learning Models to Reflect Human Knowledge and Values
Interpretability, Then What? Editing Machine Learning Models to Reflect Human Knowledge and Values
LOVE: Benchmarking and Evaluating Text-to-Video Generation and Video-to-Text Interpretation
A Graph-Based Method for Soccer Action Spotting Using Unsupervised Player Classification
Zero-Shot Temporal Interaction Localization for Egocentric Videos
No-Reference Image Quality Assessment via Transformers, Relative Ranking, and Self-Consistency
EgoExo-Fitness: Towards Egocentric and Exocentric Full-Body Action Understanding
ReferDINO-Plus: 2nd Solution for 4th PVUW MeViS Challenge at CVPR 2025
ACM-Net: Action Context Modeling Network for Weakly-Supervised Temporal Action Localization
Efficient Model Editing with Task-Localized Sparse Fine-tuning
GLOBER: Coherent Non-autoregressive Video Generation via GLOBal Guided Video DecodER
MM-LDM: Multi-Modal Latent Diffusion Model for Sounding Video Generation
MOSO: Decomposing MOtion, Scene and Object for Video Prediction
Multiple Object Tracking with Kernelized Correlation Filters in Urban Mixed Traffic
Temporal Shuffling for Defending Deep Action Recognition Models against Adversarial Attacks
Lossless compression with state space models using bits back coding
Learning Constrained Dynamic Correlations in Spatiotemporal Graphs for Motion Prediction
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.