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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/BerserkerMother/Video-Summarization
공식 코드SELF-VS: Self-supervised Encoding Learning For Video Summarization
https://github.com/BestJuly/Inter-intra-video-contrastive-learning
공식 코드Self-supervised Video Representation Learning Using Inter-intra Contrastive Framework
https://github.com/BestJuly/Pretext-Contrastive-Learning
공식 코드Pretext-Contrastive Learning: Toward Good Practices in Self-supervised Video Representation Leaning
https://github.com/BestiVictory/AVAQ
공식 코드Deep Multimodality Learning for UAV Video Aesthetic Quality Assessment
https://github.com/Betswish/Cross-Lingual-Consistency
공식 코드Cross-Lingual Consistency of Factual Knowledge in Multilingual Language Models
https://github.com/BingfengYan/CO-MOT
공식 코드Bridging the Gap Between End-to-end and Non-End-to-end Multi-Object Tracking
https://github.com/BoChenGroup/RevSCI-net
공식 코드Memory-Efficient Network for Large-scale Video Compressive Sensing
https://github.com/BoPang1996/Deep-RNN-Framework
공식 코드https://github.com/BoPang1996/Semi-Coupled-Structure-for-visual-sequental-tasks
공식 코드Complex Sequential Understanding through the Awareness of Spatial and Temporal Concepts
https://github.com/BoSmallEar/COOLer
공식 코드COOLer: Class-Incremental Learning for Appearance-Based Multiple Object Tracking
https://github.com/BobXWu/AKEW
공식 코드https://github.com/BorealisAI/TSC-Disc-Proto
공식 코드Learning Discriminative Prototypes with Dynamic Time Warping
https://github.com/BriansIDP/video-SALMONN-o1
공식 코드video-SALMONN-o1: Reasoning-enhanced Audio-visual Large Language Model
https://github.com/BrunoSauvalle/AE-NE
공식 코드https://github.com/Buzz-Beater/EgoTaskQA
공식 코드https://github.com/CAMMA-public/ConvLSTM-Surgical-Tool-Tracker
공식 코드Weakly Supervised Convolutional LSTM Approach for Tool Tracking in Laparoscopic Videos
https://github.com/CHELSEA234/SkelNet_motion_prediction
공식 코드Human Motion Prediction via Learning Local Structure Representations and Temporal Dependencies
https://github.com/CMFiltenborg/lingually_constrained_tracking
공식 코드https://github.com/CMU-CREATE-Lab/deep-smoke-machine
공식 코드https://github.com/CMU-CREATE-Lab/video-labeling-tool
공식 코드https://github.com/CS-GangXu/TMNet
공식 코드Temporal Modulation Network for Controllable Space-Time Video Super-Resolution
https://github.com/CSfufu/VidSketch
공식 코드VidSketch: Hand-drawn Sketch-Driven Video Generation with Diffusion Control
https://github.com/CV-Lehigh/Bias_IMD
공식 코드https://github.com/CandleLabAI/MotionAura-ICLR-2025
공식 코드MotionAura: Generating High-Quality and Motion Consistent Videos using Discrete Diffusion
https://github.com/CelebV-Text/CelebV-Text
공식 코드https://github.com/Chain123/Meta-HAR
공식 코드Meta-HAR: Federated Representation Learning for Human Activity Recognition
https://github.com/Charrrrrlie/SGA-INTERACT
공식 코드https://github.com/ChenHsing/Awesome-Video-Diffusion-Models
공식 코드https://github.com/ChenyangSi/FreeU
공식 코드https://github.com/Chiaraplizz/ST-TR
공식 코드Skeleton-based Action Recognition via Spatial and Temporal Transformer Networks
https://github.com/ChristophReich1996/Multi-StyleGAN
공식 코드Multi-StyleGAN: Towards Image-Based Simulation of Time-Lapse Live-Cell Microscopy
https://github.com/Chuge0335/EDG
공식 코드https://github.com/Coding-Rod/crime_detection
공식 코드Distributed Intelligent Video Surveillance for Early Armed Robbery Detection based on Deep Learning
https://github.com/Cogito2012/CarCrashDataset
공식 코드Uncertainty-based Traffic Accident Anticipation with Spatio-Temporal Relational Learning
https://github.com/Cogito2012/OpenTAL
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.