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코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/maclong01/STEP-CATFormer
공식 코드https://github.com/madhawav/MML
공식 코드Video Moment Localization using Object Evidence and Reverse Captioning
https://github.com/madrylab/modelcomponents
공식 코드Decomposing and Editing Predictions by Modeling Model Computation
https://github.com/maftej/iisnerf
공식 코드https://github.com/maftej/rfdvc
공식 코드Radiance Field Delta Video Compression in Edge-Enabled Vehicular Metaverse
https://github.com/magic-research/PLLaVA
공식 코드PLLaVA : Parameter-free LLaVA Extension from Images to Videos for Video Dense Captioning
https://github.com/magic-research/Sa2VA
공식 코드Sa2VA: Marrying SAM2 with LLaVA for Dense Grounded Understanding of Images and Videos
https://github.com/magicffourier/vq-nerv
공식 코드VQ-NeRV: A Vector Quantized Neural Representation for Videos
https://github.com/magnux/MotionGAN
공식 코드https://github.com/magref-video/magref
공식 코드https://github.com/mail-research/cad-attribution4diffusion
공식 코드https://github.com/maincold2/CAM
공식 코드https://github.com/maincold2/FFNeRV
공식 코드FFNeRV: Flow-Guided Frame-Wise Neural Representations for Videos
https://github.com/makecent/apn
공식 코드https://github.com/makinyilmaz/DFPN
공식 코드https://github.com/manantomar/video-occupancy-models
공식 코드https://github.com/maoyunyao/cmd
공식 코드CMD: Self-supervised 3D Action Representation Learning with Cross-modal Mutual Distillation
https://github.com/maoyunyao/joint
공식 코드Joint Inductive and Transductive Learning for Video Object Segmentation
https://github.com/maoyunyao/mamp
공식 코드Masked Motion Predictors are Strong 3D Action Representation Learners
https://github.com/map-learning/tl2la
공식 코드Semantic Map Learning of Traffic Light to Lane Assignment based on Motion Data
https://github.com/maple-aigc/inflvg
공식 코드InfLVG: Reinforce Inference-Time Consistent Long Video Generation with GRPO
https://github.com/mar-cry/ophora
공식 코드Ophora: A Large-Scale Data-Driven Text-Guided Ophthalmic Surgical Video Generation Model
https://github.com/mareikethies/geometry_gradients_ct
공식 코드A gradient-based approach to fast and accurate head motion compensation in cone-beam CT
https://github.com/marinero4972/cyberv
공식 코드CyberV: Cybernetics for Test-time Scaling in Video Understanding
https://github.com/mariusbock/context_har
공식 코드DeepConvContext: A Multi-Scale Approach to Timeseries Classification in Human Activity Recognition
https://github.com/mariusbock/tal_for_har
공식 코드Temporal Action Localization for Inertial-based Human Activity Recognition
https://github.com/mariusbock/wear
공식 코드WEAR: An Outdoor Sports Dataset for Wearable and Egocentric Activity Recognition
https://github.com/mark12ding/sam2long
공식 코드SAM2Long: Enhancing SAM 2 for Long Video Segmentation with a Training-Free Memory Tree
https://github.com/mark12ding/sta
공식 코드Prune Spatio-temporal Tokens by Semantic-aware Temporal Accumulation
https://github.com/marmot-xy/spoken-video-grounding
공식 코드https://github.com/martinetoering/ViCC
공식 코드Self-supervised Video Representation Learning with Cross-Stream Prototypical Contrasting
https://github.com/martinxm/gap
공식 코드Generative Action Description Prompts for Skeleton-based Action Recognition
https://github.com/martinxm/lst
공식 코드Generative Action Description Prompts for Skeleton-based Action Recognition
https://github.com/martinxm/tps
공식 코드Spatiotemporal Self-attention Modeling with Temporal Patch Shift for Action Recognition
https://github.com/martkartasev/sepconv
공식 코드Implementing Adaptive Separable Convolution for Video Frame Interpolation
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.