https://github.com/supertyd/XTrack
공식 코드XTrack: Multimodal Training Boosts RGB-X Video Object Trackers
Papers with Code 아카이브
Video
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 34
- 평가표
- 0
- 보존된 평가 기록
- 0
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/superz678/mg-had
공식 코드https://github.com/suraj-nair-1/google-research/tree/master/hierarchical_foresight
공식 코드Hierarchical Foresight: Self-Supervised Learning of Long-Horizon Tasks via Visual Subgoal Generation
https://github.com/surrey-nlp/Video-vs-Meme-Hate
공식 코드Towards a Robust Framework for Multimodal Hate Detection: A Study on Video vs. Image-based Content
https://github.com/susanqq/Talking_Face_Generation
공식 코드Talking Face Generation by Conditional Recurrent Adversarial Network
https://github.com/sustech-isus/sustechgan
공식 코드SUSTechGAN: Image Generation for Object Detection in Adverse Conditions of Autonomous Driving
https://github.com/suyukun666/UFO
공식 코드https://github.com/svip-lab/IVOS-W
공식 코드Learning to Recommend Frame for Interactive Video Object Segmentation in the Wild
https://github.com/svip-lab/weaksvr
공식 코드Weakly Supervised Video Representation Learning with Unaligned Text for Sequential Videos
https://github.com/swathikirans/LSTA
공식 코드LSTA: Long Short-Term Attention for Egocentric Action Recognition
https://github.com/swathikirans/gsf
공식 코드https://github.com/swc-17/drivecamsim
공식 코드DriveCamSim: Generalizable Camera Simulation via Explicit Camera Modeling for Autonomous Driving
https://github.com/swc-17/sparsedrive
공식 코드SparseDrive: End-to-End Autonomous Driving via Sparse Scene Representation
https://github.com/sweetorangezhuyan/mm2023_oti
공식 코드Orthogonal Temporal Interpolation for Zero-Shot Video Recognition
https://github.com/swordlidev/LLaVA-MR
공식 코드LLaVA-MR: Large Language-and-Vision Assistant for Video Moment Retrieval
https://github.com/sycny/rae
공식 코드Retrieval-enhanced Knowledge Editing in Language Models for Multi-Hop Question Answering
https://github.com/sydney0zq/PTSNet
공식 코드Proposal, Tracking and Segmentation (PTS): A Cascaded Network for Video Object Segmentation
https://github.com/syedhammadahmed/mob
공식 코드Malicious or Benign? Towards Effective Content Moderation for Children's Videos
https://github.com/syon-li/coachhook
공식 코드https://github.com/syscv/sam-pt
공식 코드https://github.com/syscv/trafficbots
공식 코드TrafficBots: Towards World Models for Autonomous Driving Simulation and Motion Prediction
https://github.com/szcf-weiya/TriMatchMOT
공식 코드Appearance-free Tripartite Matching for Multiple Object Tracking
https://github.com/t-montes/viqagent
공식 코드ViQAgent: Zero-Shot Video Question Answering via Agent with Open-Vocabulary Grounding Validation
https://github.com/tabguigui/fiptr
공식 코드https://github.com/tacju/axial-vs
공식 코드A Simple Video Segmenter by Tracking Objects Along Axial Trajectories
https://github.com/tacju/maxtron
공식 코드A Simple Video Segmenter by Tracking Objects Along Axial Trajectories
https://github.com/taco-group/genai4ad
공식 코드Generative AI for Autonomous Driving: Frontiers and Opportunities
https://github.com/taco-group/video-quality-assessment-a-comprehensive-survey
공식 코드https://github.com/taeoh-kim/temporal_data_augmentation
공식 코드Learning Temporally Invariant and Localizable Features via Data Augmentation for Video Recognition
https://github.com/tailin1009/dualhead-network
공식 코드Learning Multi-Granular Spatio-Temporal Graph Network for Skeleton-based Action Recognition
https://github.com/tal-aurorax/scen
공식 코드https://github.com/talalwasim/video-focalnets
공식 코드Video-FocalNets: Spatio-Temporal Focal Modulation for Video Action Recognition
https://github.com/talalwasim/vita-clip
공식 코드Vita-CLIP: Video and text adaptive CLIP via Multimodal Prompting
https://github.com/taldatech/ddlp
공식 코드DDLP: Unsupervised Object-Centric Video Prediction with Deep Dynamic Latent Particles
https://github.com/taldatech/deep-latent-particles-pytorch
공식 코드Unsupervised Image Representation Learning with Deep Latent Particles
https://github.com/tang1705/seeclear-neurips24
공식 코드SeeClear: Semantic Distillation Enhances Pixel Condensation for Video Super-Resolution
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.