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Image Classification
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Image Classification is a fundamental task in vision recognition that aims to understand and categorize an image as a whole under a specific label. Unlike object detection, which involves classification and location of multiple objects within an image, image classification typically pertains to single-object images. When the classification becomes highly detailed or reaches instance-level, it is often referred to as image retrieval, which also involves finding similar images in a large database.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/WinFrand/MELM
공식 코드Min-Entropy Latent Model for Weakly Supervised Object Detection
https://github.com/WonderSeven/ULDA
공식 코드Diversity Helps: Unsupervised Few-shot Learning via Distribution Shift-based Data Augmentation
https://github.com/WongKinYiu/CrossStagePartialNetworks
공식 코드CSPNet: A New Backbone that can Enhance Learning Capability of CNN
https://github.com/X1AOX1A/SeTAR
공식 코드SeTAR: Out-of-Distribution Detection with Selective Low-Rank Approximation
https://github.com/XD7479/Multi-Object-Occlusion
공식 코드Robust Instance Segmentation through Reasoning about Multi-Object Occlusion
https://github.com/XavierXiao/Likelihood-Regret
공식 코드Likelihood Regret: An Out-of-Distribution Detection Score For Variational Auto-encoder
https://github.com/XingangPan/Switchable-Whitening
공식 코드https://github.com/XinshaoAmosWang/Improving-Mean-Absolute-Error-against-CCE
공식 코드https://github.com/XiongweiWu/Awesome-Object-Detection
공식 코드https://github.com/Xu-Jingyi/PUDistill
공식 코드Training Classifiers that are Universally Robust to All Label Noise Levels
https://github.com/XuZhengzhuo/Prior-LT
공식 코드Towards Calibrated Model for Long-Tailed Visual Recognition from Prior Perspective
https://github.com/XudongLinthu/context-gated-convolution
공식 코드https://github.com/XueZ-phd/PR-FocusNet-PyTorch-ILSVRC2012
공식 코드FocusNet: Classifying Better by Focusing on Confusing Classes
https://github.com/XuyangSHEN/Non-binary-deep-transfer-learning-for-image-classification
공식 코드https://github.com/Yagami123/Caffe-AdamW-AdamWR
공식 코드https://github.com/YancyKahn/CIA
공식 코드https://github.com/YangHM/Convolutional-Prototype-Learning
공식 코드https://github.com/YangYongJin/APEX
공식 코드Towards Difficulty-Agnostic Efficient Transfer Learning for Vision-Language Models
https://github.com/YanghuiSong/DS_FusionNet
공식 코드https://github.com/YashSharma/C2C
공식 코드https://github.com/Yejin0111/ADD-GCN
공식 코드Attention-Driven Dynamic Graph Convolutional Network for Multi-Label Image Recognition
https://github.com/YifanXu74/Evo-ViT
공식 코드Evo-ViT: Slow-Fast Token Evolution for Dynamic Vision Transformer
https://github.com/Yikai-Wang/ICI-FSL
공식 코드https://github.com/YimianDai/open-aff
공식 코드https://github.com/YisenWang/symmetric_cross_entropy_for_noisy_labels
공식 코드Symmetric Cross Entropy for Robust Learning with Noisy Labels
https://github.com/Yonghongwei/Gradient-Centralization
공식 코드Gradient Centralization: A New Optimization Technique for Deep Neural Networks
https://github.com/YuMJie/ConCutMix
공식 코드Enhanced Long-Tailed Recognition with Contrastive CutMix Augmentation
https://github.com/YubiaoYue/MedMamba
공식 코드https://github.com/YuchenJin/autolrs
공식 코드AutoLRS: Automatic Learning-Rate Schedule by Bayesian Optimization on the Fly
https://github.com/YugeTen/fish
공식 코드https://github.com/YujiaBao/Predict-then-Interpolate
공식 코드Predict then Interpolate: A Simple Algorithm to Learn Stable Classifiers
https://github.com/YuntianChen/EnLSTM
공식 코드https://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/Z-Zheng/FreeNet
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.