https://github.com/fmthoker/severe-benchmark
공식 코드How Severe is Benchmark-Sensitivity in Video Self-Supervised Learning?
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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/fmthoker/skeleton-contrast
공식 코드https://github.com/foolwood/DRL
공식 코드Disentangled Representation Learning for Text-Video Retrieval
https://github.com/foolwood/DaSiamRPN
공식 코드Distractor-aware Siamese Networks for Visual Object Tracking
https://github.com/forAcadamic/T-CAD
공식 코드TAD: A Large-Scale Benchmark for Traffic Accidents Detection from Video Surveillance
https://github.com/fordcvresearch/fir-trajectory-prediction
공식 코드FIR-based Future Trajectory Prediction in Nighttime Autonomous Driving
https://github.com/foundationvision/flashvideo
공식 코드FlashVideo:Flowing Fidelity to Detail for Efficient High-Resolution Video Generation
https://github.com/foundationvision/omnitokenizer
공식 코드OmniTokenizer: A Joint Image-Video Tokenizer for Visual Generation
https://github.com/foundationvision/uniref
공식 코드UniRef++: Segment Every Reference Object in Spatial and Temporal Spaces
https://github.com/fpv-iplab/differentiable-task-graph-learning
공식 코드https://github.com/fpv-iplab/easg
공식 코드Action Scene Graphs for Long-Form Understanding of Egocentric Videos
https://github.com/fpv-iplab/rulstm
공식 코드https://github.com/fqhank/FuSTAL
공식 코드Full-Stage Pseudo Label Quality Enhancement for Weakly-supervised Temporal Action Localization
https://github.com/francis-rings/ila
공식 코드Implicit Temporal Modeling with Learnable Alignment for Video Recognition
https://github.com/franklear/lime
공식 코드https://github.com/frankniujc/kn_thesis
공식 코드What does the Knowledge Neuron Thesis Have to do with Knowledge?
https://github.com/franktpmvu/NeighborTrack
공식 코드NeighborTrack: Improving Single Object Tracking by Bipartite Matching with Neighbor Tracklets
https://github.com/fraunhoferhhi/DeepCABAC
공식 코드DeepCABAC: A Universal Compression Algorithm for Deep Neural Networks
https://github.com/frederico-klein/cad-gas
공식 코드https://github.com/freedomintelligence/longllava
공식 코드LongLLaVA: Scaling Multi-modal LLMs to 1000 Images Efficiently via a Hybrid Architecture
https://github.com/friedrichor/TUNA
공식 코드TUNA: Comprehensive Fine-grained Temporal Understanding Evaluation on Dense Dynamic Videos
https://github.com/frostinassiky/denoiseloc
공식 코드https://github.com/fsoft-aic/unifying-global-local-feature
공식 코드Unifying Global and Local Scene Entities Modelling for Precise Action Spotting
https://github.com/fsoft-aic/waver
공식 코드https://github.com/ftkr12/rostfine
공식 코드Automated Sperm Assessment Framework and Neural Network Specialized for Sperm Video Recognition
https://github.com/fuankarion/audiovisual-person-search
공식 코드https://github.com/fubel/stmc
공식 코드Spatial-Temporal Multi-Cuts for Online Multiple-Camera Vehicle Tracking
https://github.com/fuchenustc/sifa
공식 코드https://github.com/fuchenustc/videostudio
공식 코드VideoStudio: Generating Consistent-Content and Multi-Scene Videos
https://github.com/fudan-generative-vision/champ
공식 코드Champ: Controllable and Consistent Human Image Animation with 3D Parametric Guidance
https://github.com/fudan-generative-vision/hallo2
공식 코드Hallo2: Long-Duration and High-Resolution Audio-Driven Portrait Image Animation
https://github.com/fudan-zvg/wovogen
공식 코드WoVoGen: World Volume-aware Diffusion for Controllable Multi-camera Driving Scene Generation
https://github.com/fujiry0/egosurgery
공식 코드EgoSurgery-Phase: A Dataset of Surgical Phase Recognition from Egocentric Open Surgery Videos
https://github.com/fundamentalvision/Uni-Perceiver
공식 코드Uni-Perceiver-MoE: Learning Sparse Generalist Models with Conditional MoEs
https://github.com/furkanmumcu/vlad
공식 코드https://github.com/fzh0917/STMTrack
공식 코드STMTrack: Template-free Visual Tracking with Space-time Memory Networks
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.