https://github.com/thartvigsen/grace
공식 코드Aging with GRACE: Lifelong Model Editing with Discrete Key-Value Adaptors
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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/thisisbillhe/nar
공식 코드Neighboring Autoregressive Modeling for Efficient Visual Generation
https://github.com/thswodnjs3/CSTA
공식 코드CSTA: CNN-based Spatiotemporal Attention for Video Summarization
https://github.com/thu-coai/vpo
공식 코드VPO: Aligning Text-to-Video Generation Models with Prompt Optimization
https://github.com/thu-keg/event-level-knowledge-editing
공식 코드https://github.com/thu-ml/SageAttention
공식 코드SageAttention: Accurate 8-Bit Attention for Plug-and-play Inference Acceleration
https://github.com/thu-ml/spargeattn
공식 코드SpargeAttention: Accurate and Training-free Sparse Attention Accelerating Any Model Inference
https://github.com/thu-nics/VGDFR
공식 코드VGDFR: Diffusion-based Video Generation with Dynamic Latent Frame Rate
https://github.com/thu-nics/dlfr-vae
공식 코드DLFR-VAE: Dynamic Latent Frame Rate VAE for Video Generation
https://github.com/thu-nics/framefusion
공식 코드https://github.com/thudm/cogvideo
공식 코드CogVideo: Large-scale Pretraining for Text-to-Video Generation via Transformers
https://github.com/thudm/cogvlm2
공식 코드CogVLM2: Visual Language Models for Image and Video Understanding
https://github.com/thudm/glm-4
공식 코드CogVLM2: Visual Language Models for Image and Video Understanding
https://github.com/thudm/glm-4.1v-thinking
공식 코드GLM-4.1V-Thinking: Towards Versatile Multimodal Reasoning with Scalable Reinforcement Learning
https://github.com/thudm/visionreward
공식 코드https://github.com/thuhcsi/s2g-mddiffusion
공식 코드Co-Speech Gesture Video Generation via Motion-Decoupled Diffusion Model
https://github.com/thuml/MotionRNN
공식 코드MotionRNN: A Flexible Model for Video Prediction with Spacetime-Varying Motions
https://github.com/thuml/dynamical-diffusion
공식 코드Dynamical Diffusion: Learning Temporal Dynamics with Diffusion Models
https://github.com/thuml/iVideoGPT
공식 코드https://github.com/thuml/predrnn-pytorch
공식 코드PredRNN: A Recurrent Neural Network for Spatiotemporal Predictive Learning
https://github.com/thuml/videodg
공식 코드VideoDG: Generalizing Temporal Relations in Videos to Novel Domains
https://github.com/thunil/TecoGAN
공식 코드Learning Temporal Coherence via Self-Supervision for GAN-based Video Generation
https://github.com/thunlp-mt/museg
공식 코드MUSEG: Reinforcing Video Temporal Understanding via Timestamp-Aware Multi-Segment Grounding
https://github.com/thunlp-mt/streamingbench
공식 코드StreamingBench: Assessing the Gap for MLLMs to Achieve Streaming Video Understanding
https://github.com/thunlp/acdit
공식 코드ACDiT: Interpolating Autoregressive Conditional Modeling and Diffusion Transformer
https://github.com/thunlp/eren
공식 코드https://github.com/tianxingwu/freeinit
공식 코드FreeInit: Bridging Initialization Gap in Video Diffusion Models
https://github.com/tianyu0207/RTFM
공식 코드Weakly-supervised Video Anomaly Detection with Robust Temporal Feature Magnitude Learning
https://github.com/tianyuan168326/SelfC
공식 코드https://github.com/tianyuan168326/vcs-pytorch
공식 코드A Coding Framework and Benchmark towards Low-Bitrate Video Understanding
https://github.com/tianyuan168326/videosemanticcompression-pytorch
공식 코드SMC++: Masked Learning of Unsupervised Video Semantic Compression
https://github.com/tiger-ai-lab/anyv2v
공식 코드AnyV2V: A Tuning-Free Framework For Any Video-to-Video Editing Tasks
https://github.com/tiger-ai-lab/quickvideo
공식 코드QuickVideo: Real-Time Long Video Understanding with System Algorithm Co-Design
https://github.com/tigerchen52/qrneuron
공식 코드Identifying Query-Relevant Neurons in Large Language Models for Long-Form Texts
https://github.com/tileb1/motion-prediction-tim
공식 코드https://github.com/timemarker-llm/timemarker
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.