https://github.com/opendrivelab/vista
공식 코드Vista: A Generalizable Driving World Model with High Fidelity and Versatile Controllability
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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/opengvlab/ask-anything
공식 코드https://github.com/opengvlab/efficient-video-recognition
공식 코드https://github.com/opengvlab/ego4d-eccv2022-solutions
공식 코드InternVideo-Ego4D: A Pack of Champion Solutions to Ego4D Challenges
https://github.com/opengvlab/egoexolearn
공식 코드https://github.com/opengvlab/fluxvit
공식 코드Make Your Training Flexible: Towards Deployment-Efficient Video Models
https://github.com/opengvlab/hulk
공식 코드Hulk: A Universal Knowledge Translator for Human-Centric Tasks
https://github.com/opengvlab/humanbench
공식 코드Hulk: A Universal Knowledge Translator for Human-Centric Tasks
https://github.com/opengvlab/internvideo
공식 코드InternVideo: General Video Foundation Models via Generative and Discriminative Learning
https://github.com/opengvlab/internvideo2
공식 코드InternVideo2: Scaling Foundation Models for Multimodal Video Understanding
https://github.com/opengvlab/internvl
공식 코드InternVL: Scaling up Vision Foundation Models and Aligning for Generic Visual-Linguistic Tasks
https://github.com/opengvlab/mutr
공식 코드Referred by Multi-Modality: A Unified Temporal Transformer for Video Object Segmentation
https://github.com/opengvlab/phygenbench
공식 코드Towards World Simulator: Crafting Physical Commonsense-Based Benchmark for Video Generation
https://github.com/opengvlab/unmasked_teacher
공식 코드Unmasked Teacher: Towards Training-Efficient Video Foundation Models
https://github.com/opengvlab/video-mamba-suite
공식 코드Video Mamba Suite: State Space Model as a Versatile Alternative for Video Understanding
https://github.com/opengvlab/videomamba
공식 코드VideoMamba: State Space Model for Efficient Video Understanding
https://github.com/opengvlab/vinci
공식 코드Vinci: A Real-time Embodied Smart Assistant based on Egocentric Vision-Language Model
https://github.com/openimaginglab/egvd
공식 코드EGVD: Event-Guided Video Diffusion Model for Physically Realistic Large-Motion Frame Interpolation
https://github.com/openmotionlab/motiongpt
공식 코드https://github.com/opennlplab/favdbench
공식 코드https://github.com/opennlplab/tavgbench
공식 코드https://github.com/openrobotlab/egohod
공식 코드Modeling Fine-Grained Hand-Object Dynamics for Egocentric Video Representation Learning
https://github.com/opensparsellms/skip-dit
공식 코드https://github.com/openvinotoolkit/training_extensions
공식 코드https://github.com/orangeeji/3d-ace
공식 코드https://github.com/orangesodahub/orv
공식 코드https://github.com/oravus/DeltaDescriptors
공식 코드Delta Descriptors: Change-Based Place Representation for Robust Visual Localization
https://github.com/oravus/seqNet
공식 코드SeqNet: Learning Descriptors for Sequence-based Hierarchical Place Recognition
https://github.com/orhir/stg-nf
공식 코드https://github.com/orrzohar/Video-STaR
공식 코드Video-STaR: Self-Training Enables Video Instruction Tuning with Any Supervision
https://github.com/oskyhn/CNNs-Without-Borders
공식 코드On Translation Invariance in CNNs: Convolutional Layers can Exploit Absolute Spatial Location
https://github.com/ostadabbas/multiple-toddler-tracking
공식 코드https://github.com/ostadabbas/video-based-infant-action-recognition
공식 코드Challenges in Video-Based Infant Action Recognition: A Critical Examination of the State of the Art
https://github.com/osvai/ske2grid
공식 코드Ske2Grid: Skeleton-to-Grid Representation Learning for Action Recognition
https://github.com/outsiders17711/e2eet-skeleton-based-hgr-using-data-level-fusion
공식 코드Real-Time Hand Gesture Recognition: Integrating Skeleton-Based Data Fusion and Multi-Stream CNN
https://github.com/ouxiang-li/speed
공식 코드SPEED: Scalable, Precise, and Efficient Concept Erasure for Diffusion Models
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.