https://github.com/midas-research/video-persuasion
공식 코드A Video Is Worth 4096 Tokens: Verbalize Videos To Understand Them In Zero Shot
Papers with Code 아카이브
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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/miliadis/DeepVideoCS
공식 코드https://github.com/mimbres/SeqSkip
공식 코드Sequential Skip Prediction with Few-shot in Streamed Music Contents
https://github.com/mindspore-lab/models
공식 코드No Time to Waste: Squeeze Time into Channel for Mobile Video Understanding
https://github.com/minghanli/univs
공식 코드UniVS: Unified and Universal Video Segmentation with Prompts as Queries
https://github.com/minghchen/carl_code
공식 코드Frame-wise Action Representations for Long Videos via Sequence Contrastive Learning
https://github.com/mingxiangl/devil
공식 코드Evaluation of Text-to-Video Generation Models: A Dynamics Perspective
https://github.com/mininglamp-mllm/hmllm
공식 코드https://github.com/minjoong507/bm-detr
공식 코드Background-aware Moment Detection for Video Moment Retrieval
https://github.com/minnie-lin/awesome-physics-cognition-based-video-generation
공식 코드Exploring the Evolution of Physics Cognition in Video Generation: A Survey
https://github.com/miriambellver/refvos
공식 코드RefVOS: A Closer Look at Referring Expressions for Video Object Segmentation
https://github.com/mit-biomimetics/orcagym
공식 코드https://github.com/mit-han-lab/x-attention
공식 코드XAttention: Block Sparse Attention with Antidiagonal Scoring
https://github.com/miulab/editllm-survey
공식 코드https://github.com/miv-xjtu/artrack
공식 코드ARTrackV2: Prompting Autoregressive Tracker Where to Look and How to Describe
https://github.com/mjkwon2021/cat-net
공식 코드Learning JPEG Compression Artifacts for Image Manipulation Detection and Localization
https://github.com/mjy1111/bake
공식 코드Untying the Reversal Curse via Bidirectional Language Model Editing
https://github.com/mjy1111/peak
공식 코드Neighboring Perturbations of Knowledge Editing on Large Language Models
https://github.com/mjy1111/prune
공식 코드https://github.com/mk-minchul/caface
공식 코드Cluster and Aggregate: Face Recognition with Large Probe Set
https://github.com/mkaep/transformer-explainability
공식 코드Attention Please: What Transformer Models Really Learn for Process Prediction
https://github.com/mkg1204/rhmnet-for-ssvos
공식 코드Reliability-Hierarchical Memory Network for Scribble-Supervised Video Object Segmentation
https://github.com/mks0601/IntegralAction_RELEASE
공식 코드IntegralAction: Pose-driven Feature Integration for Robust Human Action Recognition in Videos
https://github.com/ml-jku/clamp
공식 코드Enhancing Activity Prediction Models in Drug Discovery with the Ability to Understand Human Language
https://github.com/ml-postech/skeleton-anonymization
공식 코드https://github.com/mlvccn/pla_sm_videopred
공식 코드Pair-wise Layer Attention with Spatial Masking for Video Prediction
https://github.com/mlvccn/research
공식 코드Fast Fourier Inception Networks for Occluded Video Prediction
https://github.com/mlvlab/DDMI
공식 코드https://github.com/mlvlab/MELTR
공식 코드MELTR: Meta Loss Transformer for Learning to Fine-tune Video Foundation Models
https://github.com/mlvlab/vid-tldr
공식 코드vid-TLDR: Training Free Token merging for Light-weight Video Transformer
https://github.com/mlvlab/vidchain
공식 코드VidChain: Chain-of-Tasks with Metric-based Direct Preference Optimization for Dense Video Captioning
https://github.com/mm4spa/eigd
공식 코드A Unified Taxonomy and Multimodal Dataset for Events in Invasion Games
https://github.com/mmahdavian/stpotr
공식 코드https://github.com/mnyuew/metok
공식 코드METok: Multi-Stage Event-based Token Compression for Efficient Long Video Understanding
https://github.com/modelscope/DiffSynth-Studio
공식 코드ExVideo: Extending Video Diffusion Models via Parameter-Efficient Post-Tuning
https://github.com/modelscope/data-juicer
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.