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코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/ergastialex/r-flav
공식 코드$^R$FLAV: Rolling Flow matching for infinite Audio Video generation
https://github.com/eric-ai-lab/mmworld
공식 코드MMWorld: Towards Multi-discipline Multi-faceted World Model Evaluation in Videos
https://github.com/eric-mitchell/mend
공식 코드https://github.com/eric8932/jeep
공식 코드Joint Knowledge Editing for Information Enrichment and Probability Promotion
https://github.com/ericashimomoto/parameter-efficient-tvg
공식 코드Towards Parameter-Efficient Integration of Pre-Trained Language Models In Temporal Video Grounding
https://github.com/erktkdg/MTFL
공식 코드https://github.com/esw0116/DynaVSR
공식 코드https://github.com/eth-ait/motion-transformer
공식 코드A Spatio-temporal Transformer for 3D Human Motion Prediction
https://github.com/eugenelet/crfp
공식 코드Cross-Resolution Flow Propagation for Foveated Video Super-Resolution
https://github.com/eunjnnn/bfstvsr
공식 코드https://github.com/evendrow/somoformer
공식 코드https://github.com/event-ahu/celex-har
공식 코드Event Stream based Human Action Recognition: A High-Definition Benchmark Dataset and Algorithms
https://github.com/event-ahu/hardvs
공식 코드HARDVS: Revisiting Human Activity Recognition with Dynamic Vision Sensors
https://github.com/event-ahu/tscformer
공식 코드Unleashing the Power of CNN and Transformer for Balanced RGB-Event Video Recognition
https://github.com/evgeneybogatyrev/super-resolution-metric
공식 코드SR+Codec: a Benchmark of Super-Resolution for Video Compression Bitrate Reduction
https://github.com/evolvinglmms-lab/egolife
공식 코드https://github.com/evolvinglmms-lab/lmms-eval
공식 코드https://github.com/ewrfcas/uni3c
공식 코드Uni3C: Unifying Precisely 3D-Enhanced Camera and Human Motion Controls for Video Generation
https://github.com/exisas/lgc-vd
공식 코드https://github.com/explainableml/avdiff-gfsl
공식 코드Text-to-feature diffusion for audio-visual few-shot learning
https://github.com/explainableml/egocvr
공식 코드EgoCVR: An Egocentric Benchmark for Fine-Grained Composed Video Retrieval
https://github.com/explainableml/tcaf-gzsl
공식 코드Temporal and cross-modal attention for audio-visual zero-shot learning
https://github.com/eyalnaor/DeepTemporalSR
공식 코드Across Scales & Across Dimensions: Temporal Super-Resolution using Deep Internal Learning
https://github.com/eyeline-research/survey-video-diffusion
공식 코드Survey of Video Diffusion Models: Foundations, Implementations, and Applications
https://github.com/f-ilic/AppearanceFreeActionRecognition
공식 코드https://github.com/f3set/f3set
공식 코드F$^3$Set: Towards Analyzing Fast, Frequent, and Fine-grained Events from Videos
https://github.com/fabienbaradel/glimpse_clouds
공식 코드Glimpse Clouds: Human Activity Recognition from Unstructured Feature Points
https://github.com/fabriziogaruti/TabDiT
공식 코드Diffusion Transformers for Tabular Data Time Series Generation
https://github.com/facebookresearch/ActivityNet-Entities
공식 코드https://github.com/facebookresearch/AnimatedDrawings
공식 코드A Method for Animating Children's Drawings of the Human Figure
https://github.com/facebookresearch/DepthContrast
공식 코드Self-Supervised Pretraining of 3D Features on any Point-Cloud
https://github.com/facebookresearch/Ego4d
공식 코드Ego-Exo4D: Understanding Skilled Human Activity from First- and Third-Person Perspectives
https://github.com/facebookresearch/EgoVLPv2
공식 코드EgoVLPv2: Egocentric Video-Language Pre-training with Fusion in the Backbone
https://github.com/facebookresearch/GloRe
공식 코드https://github.com/facebookresearch/Motionformer
공식 코드Keeping Your Eye on the Ball: Trajectory Attention in Video Transformers
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.