https://github.com/paolomandica/hysp
공식 코드HYperbolic Self-Paced Learning for Self-Supervised Skeleton-based Action Representations
Papers with Code 아카이브
3D Action Recognition
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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Image: Rahmani et al
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/pha123661/SA-DVAE
공식 코드https://github.com/pkorus/neural-imaging
공식 코드https://github.com/pkunlp-icler/ike
공식 코드https://github.com/polimi-ispl/icpr2020dfdc
공식 코드https://github.com/princeton-nlp/mquake
공식 코드MQuAKE: Assessing Knowledge Editing in Language Models via Multi-Hop Questions
https://github.com/proteus1991/pscc-net
공식 코드https://github.com/qilimk/biggan-am
공식 코드https://github.com/qinghua-zhou/stealth-edits
공식 코드https://github.com/qiuzh20/tuning-keys-v.s.-values
공식 코드Empirical Study on Updating Key-Value Memories in Transformer Feed-forward Layers
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/quqxui/medlasa
공식 코드Editing Factual Knowledge and Explanatory Ability of Medical Large Language Models
https://github.com/realcrane/SMART
공식 코드Understanding the Robustness of Skeleton-based Action Recognition under Adversarial Attack
https://github.com/rmokady/JOKR
공식 코드JOKR: Joint Keypoint Representation for Unsupervised Cross-Domain Motion Retargeting
https://github.com/ruizhuo-xu/skeleton2vec
공식 코드https://github.com/ruthless-man/awesome-learn-from-model
공식 코드https://github.com/rymarinelli/leaking_lora
공식 코드Leaking LoRa: An Evaluation of Password Leaks and Knowledge Storage in Large Language Models
https://github.com/s-shamil/HandFormer
공식 코드https://github.com/samyadeepbasu/locogen
공식 코드On Mechanistic Knowledge Localization in Text-to-Image Generative Models
https://github.com/sandy-zeng/pcl
공식 코드Towards Effective Image Manipulation Detection with Proposal Contrastive Learning
https://github.com/scalable-model-editing/gradual-catastrophic-forgetting
공식 코드Model Editing at Scale leads to Gradual and Catastrophic Forgetting
https://github.com/scalable-model-editing/knowledge-editing-regularization
공식 코드https://github.com/scalable-model-editing/rebuilding-rome
공식 코드Rebuilding ROME : Resolving Model Collapse during Sequential Model Editing
https://github.com/scalable-model-editing/unified-model-editing
공식 코드https://github.com/scu-zjz/forensichub
공식 코드ForensicHub: A Unified Benchmark & Codebase for All-Domain Fake Image Detection and Localization
https://github.com/scu-zjz/imdlbenco
공식 코드IMDL-BenCo: A Comprehensive Benchmark and Codebase for Image Manipulation Detection & Localization
https://github.com/sfadingz/tse_gcn
공식 코드Topological Symmetry Enhanced Graph Convolution for Skeleton-Based Action Recognition
https://github.com/shahroudy/NTURGB-D
공식 코드NTU RGB+D: A Large Scale Dataset for 3D Human Activity Analysis
https://github.com/shanakarg/stjd-spatio-temporal-joint-density-driven-learning-for-skeleton-based-action-recognition
공식 코드Spatio-Temporal Joint Density Driven Learning for Skeleton-Based Action Recognition
https://github.com/shanice-l/st-cubism
공식 코드Learning from Temporal Spatial Cubism for Cross-Dataset Skeleton-based Action Recognition
https://github.com/shankarp8/knowledge_distillation
공식 코드https://github.com/shngjz/wildcamera
공식 코드Tame a Wild Camera: In-the-Wild Monocular Camera Calibration
https://github.com/silentview/emcid
공식 코드https://github.com/sislab-unitn/dyknow
공식 코드DyKnow: Dynamically Verifying Time-Sensitive Factual Knowledge in LLMs
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.