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코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/kwwcv/ssmp
공식 코드Semi-Supervised Class-Agnostic Motion Prediction with Pseudo Label Regeneration and BEVMix
https://github.com/kylemin/A2CL-PT
공식 코드Adversarial Background-Aware Loss for Weakly-supervised Temporal Activity Localization
https://github.com/l1bra1/rigidflow
공식 코드Self-Supervised 3D Scene Flow Estimation and Motion Prediction using Local Rigidity Prior
https://github.com/l8cv/busterx
공식 코드BusterX: MLLM-Powered AI-Generated Video Forgery Detection and Explanation
https://github.com/lRomul/ball-action-spotting
공식 코드https://github.com/la30/agflow
공식 코드https://github.com/labribkb/fem
공식 코드https://github.com/labshuhanggu/lrti-vsr
공식 코드https://github.com/labshuhanggu/mia-vsr
공식 코드Video Super-Resolution Transformer with Masked Inter&Intra-Frame Attention
https://github.com/lambert-x/video-semisup
공식 코드Learning from Temporal Gradient for Semi-supervised Action Recognition
https://github.com/lancasterli/refsam
공식 코드RefSAM: Efficiently Adapting Segmenting Anything Model for Referring Video Object Segmentation
https://github.com/lancopku/mr-vpc
공식 코드Towards Multimodal Video Paragraph Captioning Models Robust to Missing Modality
https://github.com/landiff/landiff
공식 코드The Best of Both Worlds: Integrating Language Models and Diffusion Models for Video Generation
https://github.com/lanmng/sitvos
공식 코드Siamese Network with Interactive Transformer for Video Object Segmentation
https://github.com/laomao0/BIN
공식 코드https://github.com/lapid92/gd-vdm
공식 코드GD-VDM: Generated Depth for better Diffusion-based Video Generation
https://github.com/lark-png/acvubench
공식 코드https://github.com/laura-wang/video-pace
공식 코드Self-supervised Video Representation Learning by Pace Prediction
https://github.com/laura-wang/video_repres_mas
공식 코드https://github.com/laura-wang/video_repres_sts
공식 코드Self-supervised Video Representation Learning by Uncovering Spatio-temporal Statistics
https://github.com/laurarkart/Physical-Anomalous-Trajectory-or-Motion-PHANTOM-Dataset
공식 코드Approaches Toward Physical and General Video Anomaly Detection
https://github.com/lauziere/mhht
공식 코드https://github.com/lavi-lab/aim
공식 코드AIM: Adaptive Inference of Multi-Modal LLMs via Token Merging and Pruning
https://github.com/lavi-lab/tg-vid
공식 코드https://github.com/layer6ai-labs/ASL
공식 코드https://github.com/layer6ai-labs/xpool
공식 코드X-Pool: Cross-Modal Language-Video Attention for Text-Video Retrieval
https://github.com/ldarrick/PathSignatures
공식 코드https://github.com/ldenninger/camcontexti2v
공식 코드https://github.com/leaplabthu/adafocusv2
공식 코드AdaFocus V2: End-to-End Training of Spatial Dynamic Networks for Video Recognition
https://github.com/leaplabthu/cross-modal-adapter
공식 코드https://github.com/leaplabthu/uni-adafocus
공식 코드Uni-AdaFocus: Spatial-temporal Dynamic Computation for Video Recognition
https://github.com/leexinhao/VideoEval
공식 코드VideoEval: Comprehensive Benchmark Suite for Low-Cost Evaluation of Video Foundation Model
https://github.com/leexinhao/ZeroI2V
공식 코드ZeroI2V: Zero-Cost Adaptation of Pre-trained Transformers from Image to Video
https://github.com/leeyn-43/clover
공식 코드Clover: Towards A Unified Video-Language Alignment and Fusion Model
https://github.com/leipingjie/sam2video
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.