https://github.com/qiuzh20/tuning-keys-v.s.-values
공식 코드Empirical Study on Updating Key-Value Memories in Transformer Feed-forward Layers
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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/qizhou000/DAFNet
공식 코드DAFNet: Dynamic Auxiliary Fusion for Sequential Model Editing in Large Language Models
https://github.com/qizhou000/RECIPE
공식 코드Lifelong Knowledge Editing for LLMs with Retrieval-Augmented Continuous Prompt Learning
https://github.com/qizhou000/visedit
공식 코드https://github.com/qmme/stablevqa
공식 코드StableVQA: A Deep No-Reference Quality Assessment Model for Video Stability
https://github.com/qmme/t2vqa
공식 코드Subjective-Aligned Dataset and Metric for Text-to-Video Quality Assessment
https://github.com/qq-mm/video-ccam
공식 코드https://github.com/qualityassessment/bvqi
공식 코드Exploring Opinion-unaware Video Quality Assessment with Semantic Affinity Criterion
https://github.com/qualityminds/cistgcn
공식 코드Context-based Interpretable Spatio-Temporal Graph Convolutional Network for Human Motion Forecasting
https://github.com/quan-dao/pc-corrector
공식 코드Aligning Bird-Eye View Representation of Point Cloud Sequences using Scene Flow
https://github.com/quandao10/slct
공식 코드https://github.com/quangminhdinh/trafficvlm
공식 코드TrafficVLM: A Controllable Visual Language Model for Traffic Video Captioning
https://github.com/quangtrungtruong/KIS_MVK
공식 코드Marine Video Kit: A New Marine Video Dataset for Content-based Analysis and Retrieval
https://github.com/quqxui/medlasa
공식 코드Editing Factual Knowledge and Explanatory Ability of Medical Large Language Models
https://github.com/qyp2000/kvq
공식 코드KVQ: Boosting Video Quality Assessment via Saliency-guided Local Perception
https://github.com/qyr0403/reversed-in-time
공식 코드Reversed in Time: A Novel Temporal-Emphasized Benchmark for Cross-Modal Video-Text Retrieval
https://github.com/qzhb/bssard
공식 코드https://github.com/raghavgoyal14/minotaur/tree/rgv/all-tasks
공식 코드MINOTAUR: Multi-task Video Grounding From Multimodal Queries
https://github.com/raingo/TGIF-Release
공식 코드TGIF: A New Dataset and Benchmark on Animated GIF Description
https://github.com/rakhimovv/lvt
공식 코드https://github.com/ramtin-ma/videosynopsis-fgs
공식 코드A Low-Computational Video Synopsis Framework with a Standard Dataset
https://github.com/rana2149/actnetformer
공식 코드ActNetFormer: Transformer-ResNet Hybrid Method for Semi-Supervised Action Recognition in Videos
https://github.com/rasoolfa/videocap
공식 코드https://github.com/rate-accuracy-mvcnn/main
공식 코드Rate-Accuracy Trade-Off In Video Classification With Deep Convolutional Neural Networks
https://github.com/rayabhisek123/autoregad-hgformer
공식 코드Autoregressive Adaptive Hypergraph Transformer for Skeleton-based Activity Recognition
https://github.com/rd20karim/synch-transformer
공식 코드Transformer with Controlled Attention for Synchronous Motion Captioning
https://github.com/realcrane/SMART
공식 코드Understanding the Robustness of Skeleton-based Action Recognition under Adversarial Attack
https://github.com/reddyav1/rocog-v2
공식 코드Synthetic-to-Real Domain Adaptation for Action Recognition: A Dataset and Baseline Performances
https://github.com/reddyav1/unite
공식 코드Unsupervised Video Domain Adaptation with Masked Pre-Training and Collaborative Self-Training
https://github.com/redrock303/revisiting-temporal-alignment-for-video-restoration
공식 코드https://github.com/renjie-liang/emet
공식 코드Efficient Temporal Sentence Grounding in Videos with Multi-Teacher Knowledge Distillation
https://github.com/renshuhuai-andy/testa
공식 코드TESTA: Temporal-Spatial Token Aggregation for Long-form Video-Language Understanding
https://github.com/renshuhuai-andy/timechat
공식 코드TimeChat: A Time-sensitive Multimodal Large Language Model for Long Video Understanding
https://github.com/renyang-home/alvc
공식 코드Advancing Learned Video Compression with In-loop Frame Prediction
https://github.com/renyang-home/plvc
공식 코드Perceptual Learned Video Compression with Recurrent Conditional GAN
https://github.com/rerv/uniadapter
공식 코드UniAdapter: Unified Parameter-Efficient Transfer Learning for Cross-modal Modeling
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.