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코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/stepfun-ai/step-video-t2v
공식 코드Step-Video-T2V Technical Report: The Practice, Challenges, and Future of Video Foundation Model
https://github.com/stepfun-ai/step-video-ti2v
공식 코드Step-Video-TI2V Technical Report: A State-of-the-Art Text-Driven Image-to-Video Generation Model
https://github.com/stergiosbamp/deep-physical-activity-prediction
공식 코드https://github.com/steve-tod/stfc3
공식 코드Breaking Shortcut: Exploring Fully Convolutional Cycle-Consistency for Video Correspondence Learning
https://github.com/steveliao93/gcn_logsigrnn
공식 코드Logsig-RNN: a novel network for robust and efficient skeleton-based action recognition
https://github.com/steven-ccq/vilamp
공식 코드Scaling Video-Language Models to 10K Frames via Hierarchical Differential Distillation
https://github.com/stevenliuwen/ano_pred_cvpr2018
공식 코드Future Frame Prediction for Anomaly Detection -- A New Baseline
https://github.com/stevenlsw/physgen
공식 코드PhysGen: Rigid-Body Physics-Grounded Image-to-Video Generation
https://github.com/sthoduka/handover_failure_detection
공식 코드A Multimodal Handover Failure Detection Dataset and Baselines
https://github.com/stnoah1/infogcn2
공식 코드https://github.com/stnoah1/sode
공식 코드https://github.com/stoa-xh91/acfl
공식 코드Skeleton-based Action Recognition via Adaptive Cross-Form Learning
https://github.com/sts-vlcc/sts-vlcc
공식 코드https://github.com/stuarteiffert/MCTS-GRNN
공식 코드Path Planning in Dynamic Environments using Generative RNNs and Monte Carlo Tree Search
https://github.com/suakaw/bdq_privacyar
공식 코드Privacy-Preserving Action Recognition via Motion Difference Quantization
https://github.com/subin-kim-cv/NVP
공식 코드Scalable Neural Video Representations with Learnable Positional Features
https://github.com/sudo-Boris/mr-Blip
공식 코드The Surprising Effectiveness of Multimodal Large Language Models for Video Moment Retrieval
https://github.com/suhaisheng/difsd
공식 코드https://github.com/suhwan-cho/FindTrack
공식 코드https://github.com/suhwan-cho/bmvos
공식 코드Pixel-Level Bijective Matching for Video Object Segmentation
https://github.com/suhwan-cho/tbd
공식 코드Tackling Background Distraction in Video Object Segmentation
https://github.com/suhwan-cho/tmo
공식 코드Treating Motion as Option to Reduce Motion Dependency in Unsupervised Video Object Segmentation
https://github.com/sujoyp/wtalc-pytorch
공식 코드W-TALC: Weakly-supervised Temporal Activity Localization and Classification
https://github.com/sunilhoho/VideoMS
공식 코드EVEREST: Efficient Masked Video Autoencoder by Removing Redundant Spatiotemporal Tokens
https://github.com/sunilhoho/everest
공식 코드EVEREST: Efficient Masked Video Autoencoder by Removing Redundant Spatiotemporal Tokens
https://github.com/sunoh-kim/pps
공식 코드https://github.com/sunwei925/CompressedVQA
공식 코드https://github.com/sunwei925/SimpleVQA
공식 코드A Deep Learning based No-reference Quality Assessment Model for UGC Videos
https://github.com/sunwei925/dn-piqa
공식 코드https://github.com/sunwei925/minimalisticvqa
공식 코드Analysis of Video Quality Datasets via Design of Minimalistic Video Quality Models
https://github.com/sunwei925/rq-vqa
공식 코드Enhancing Blind Video Quality Assessment with Rich Quality-aware Features
https://github.com/sunxm2357/TAI_video_frame_inpainting
공식 코드A Temporally-Aware Interpolation Network for Video Frame Inpainting
https://github.com/sunxm2357/TwoStreamVAN
공식 코드https://github.com/sunyoungIT/UDCA-Net
공식 코드https://github.com/sunzhihao18/ForgerySleuth
공식 코드ForgerySleuth: Empowering Multimodal Large Language Models for Image Manipulation Detection
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.