https://github.com/BIT-DA/MetaSAug
공식 코드MetaSAug: Meta Semantic Augmentation for Long-Tailed Visual Recognition
Papers with Code 아카이브
Generalized Few-Shot Classification
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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/ChangyaoTian/VL-LTR
공식 코드VL-LTR: Learning Class-wise Visual-Linguistic Representation for Long-Tailed Visual Recognition
https://github.com/Jia-Research-Lab/MiSLAS
공식 코드https://github.com/Jianf-Wang/RSG
공식 코드RSG: A Simple but Effective Module for Learning Imbalanced Datasets
https://github.com/KMnP/vpt
공식 코드https://github.com/KaihuaTang/Long-Tailed-Recognition.pytorch
공식 코드Long-Tailed Classification by Keeping the Good and Removing the Bad Momentum Causal Effect
https://github.com/Megvii-BaseDetection/DisAlign
공식 코드Distribution Alignment: A Unified Framework for Long-tail Visual Recognition
https://github.com/Megvii-Nanjing/BBN
공식 코드BBN: Bilateral-Branch Network with Cumulative Learning for Long-Tailed Visual Recognition
https://github.com/NeuralCollapseApplications/ImbalancedLearning
공식 코드https://github.com/Vanint/TADE-AgnosticLT
공식 코드Self-Supervised Aggregation of Diverse Experts for Test-Agnostic Long-Tailed Recognition
https://github.com/XuZhengzhuo/Prior-LT
공식 코드Towards Calibrated Model for Long-Tailed Visual Recognition from Prior Perspective
https://github.com/YuMJie/ConCutMix
공식 코드Enhanced Long-Tailed Recognition with Contrastive CutMix Augmentation
https://github.com/YutingLi0606/SURE
공식 코드SURE: SUrvey REcipes for building reliable and robust deep networks
https://github.com/YyzHarry/imbalanced-semi-self
공식 코드Rethinking the Value of Labels for Improving Class-Imbalanced Learning
https://github.com/abdullahjamal/Longtail_DA
공식 코드https://github.com/alcunha/bagoftricks4cameratraps
공식 코드Bag of Tricks for Long-Tail Visual Recognition of Animal Species in Camera-Trap Images
https://github.com/alinlab/M2m
공식 코드M2m: Imbalanced Classification via Major-to-minor Translation
https://github.com/allanyangzhou/generative-invariance-transfer
공식 코드https://github.com/bazinga699/ncl
공식 코드Nested Collaborative Learning for Long-Tailed Visual Recognition
https://github.com/bluecdm/Long-tailed-recognition
공식 코드https://github.com/boyuh/aucseg
공식 코드AUCSeg: AUC-oriented Pixel-level Long-tail Semantic Segmentation
https://github.com/dgshin21/rna
공식 코드Representation Norm Amplification for Out-of-Distribution Detection in Long-Tail Learning
https://github.com/dongsky/lpt
공식 코드https://github.com/dvirsamuel/DRAGON
공식 코드From Generalized zero-shot learning to long-tail with class descriptors
https://github.com/dvirsamuel/SeedSelect
공식 코드Norm-guided latent space exploration for text-to-image generation
https://github.com/dvlab-research/parametric-contrastive-learning
공식 코드https://github.com/dvlab-research/rescom
공식 코드Rebalanced Siamese Contrastive Mining for Long-Tailed Recognition
https://github.com/emasa/balpoe-calibratedlt
공식 코드Balanced Product of Calibrated Experts for Long-Tailed Recognition
https://github.com/facebookresearch/classifier-balancing
공식 코드Decoupling Representation and Classifier for Long-Tailed Recognition
https://github.com/facebookresearch/detectron
공식 코드https://github.com/fistyee/LTRL
공식 코드LTRL: Boosting Long-tail Recognition via Reflective Learning
https://github.com/fistyee/mdcs
공식 코드MDCS: More Diverse Experts with Consistency Self-distillation for Long-tailed Recognition
https://github.com/flamiezhu/bcl
공식 코드Balanced Contrastive Learning for Long-Tailed Visual Recognition
https://github.com/frank-xwang/RIDE-LongTailRecognition
공식 코드Long-tailed Recognition by Routing Diverse Distribution-Aware Experts
https://github.com/gaopengcuhk/ballad
공식 코드A Simple Long-Tailed Recognition Baseline via Vision-Language Model
https://github.com/google-research/google-research
공식 코드Class-Balanced Distillation for Long-Tailed Visual Recognition
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.