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코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/nust-machine-intelligence-laboratory/hvc
공식 코드Dynamic in Static: Hybrid Visual Correspondence for Self-Supervised Video Object Segmentation
https://github.com/nust-machine-intelligence-laboratory/videomac
공식 코드https://github.com/nv-tlabs/GEN3C
공식 코드GEN3C: 3D-Informed World-Consistent Video Generation with Precise Camera Control
https://github.com/nv-tlabs/cosmos-drive-dreams
공식 코드Cosmos-Drive-Dreams: Scalable Synthetic Driving Data Generation with World Foundation Models
https://github.com/nvidia/gr00t-dreams
공식 코드DreamGen: Unlocking Generalization in Robot Learning through Video World Models
https://github.com/nvlabs/vila
공식 코드LongVILA: Scaling Long-Context Visual Language Models for Long Videos
https://github.com/nwojke/deep_sort
공식 코드Simple Online and Realtime Tracking with a Deep Association Metric
https://github.com/nycu-mapl/canf-vc
공식 코드CANF-VC: Conditional Augmented Normalizing Flows for Video Compression
https://github.com/nysp78/counterfactual-video-generation
공식 코드Causally Steered Diffusion for Automated Video Counterfactual Generation
https://github.com/ocetintas/mpntrackseg
공식 코드Multi-Object Tracking and Segmentation via Neural Message Passing
https://github.com/ohsngjun/PEVC
공식 코드Parameter-Efficient Instance-Adaptive Neural Video Compression
https://github.com/ok1zjf/VASNet
공식 코드https://github.com/okankop/Dissected-3D-CNNs
공식 코드Dissected 3D CNNs: Temporal Skip Connections for Efficient Online Video Processing
https://github.com/okankop/MFF-pytorch
공식 코드Motion Fused Frames: Data Level Fusion Strategy for Hand Gesture Recognition
https://github.com/oliverhxh/skeletongcl
공식 코드Graph Contrastive Learning for Skeleton-based Action Recognition
https://github.com/oliverrensu/grat
공식 코드Grouping First, Attending Smartly: Training-Free Acceleration for Diffusion Transformers
https://github.com/olivesgatech/gradtrust
공식 코드Counterfactual Gradients-based Quantification of Prediction Trust in Neural Networks
https://github.com/oliviazwy/dcgcn
공식 코드Dynamic Compositional Graph Convolutional Network for Efficient Composite Human Motion Prediction
https://github.com/om-ai-lab/OmAgent
공식 코드OmAgent: A Multi-modal Agent Framework for Complex Video Understanding with Task Divide-and-Conquer
https://github.com/ombretta/longterm_datasets
공식 코드Are current long-term video understanding datasets long-term?
https://github.com/ombretta/videobagnet
공식 코드Video BagNet: short temporal receptive fields increase robustness in long-term action recognition
https://github.com/omkarthawakar/composed-video-retrieval
공식 코드Composed Video Retrieval via Enriched Context and Discriminative Embeddings
https://github.com/omron-sinicx/com_kitchens
공식 코드COM Kitchens: An Unedited Overhead-view Video Dataset as a Vision-Language Benchmark
https://github.com/oooolga/Ctrl-V
공식 코드Ctrl-V: Higher Fidelity Video Generation with Bounding-Box Controlled Object Motion
https://github.com/oooolga/JEDi
공식 코드Beyond FVD: Enhanced Evaluation Metrics for Video Generation Quality
https://github.com/opanhw/FiNE
공식 코드Precise Localization of Memories: A Fine-grained Neuron-level Knowledge Editing Technique for LLMs
https://github.com/opanhw/MM_Neurons
공식 코드Finding and Editing Multi-Modal Neurons in Pre-Trained Transformers
https://github.com/open-compass/vlmevalkit
공식 코드MMBench-Video: A Long-Form Multi-Shot Benchmark for Holistic Video Understanding
https://github.com/open-mmlab/foleycrafter
공식 코드FoleyCrafter: Bring Silent Videos to Life with Lifelike and Synchronized Sounds
https://github.com/open-mmlab/mmaction
공식 코드Omni-sourced Webly-supervised Learning for Video Recognition
https://github.com/open-mmlab/mmediting
공식 코드BasicVSR++: Improving Video Super-Resolution with Enhanced Propagation and Alignment
https://github.com/openai/consistency_models
공식 코드https://github.com/opendilab/smartrefine
공식 코드SmartRefine: A Scenario-Adaptive Refinement Framework for Efficient Motion Prediction
https://github.com/opendrivelab/driveagi
공식 코드https://github.com/opendrivelab/end-to-end-autonomous-driving
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.