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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/yaoyaozhu19/bsda
공식 코드BSDA: Bayesian Random Semantic Data Augmentation for Medical Image Classification
https://github.com/yassouali/awesome-semi-supervised-learning
공식 코드https://github.com/ybendou/easy
공식 코드https://github.com/ybzh/lapt
공식 코드LAPT: Label-driven Automated Prompt Tuning for OOD Detection with Vision-Language Models
https://github.com/ycaigogogo/cvpr24-coder
공식 코드Leveraging Cross-Modal Neighbor Representation for Improved CLIP Classification
https://github.com/ycfate/id-like
공식 코드ID-like Prompt Learning for Few-Shot Out-of-Distribution Detection
https://github.com/ychnlgy/DeepConsensus
공식 코드https://github.com/ychnlgy/DeepConsensus-experimental-FROZEN
공식 코드https://github.com/ydc123/Dark_Surrogate_Model
공식 코드Boosting the Adversarial Transferability of Surrogate Models with Dark Knowledge
https://github.com/yebo0216best/lps-main
공식 코드Bridging the Gap: Learning Pace Synchronization for Open-World Semi-Supervised Learning
https://github.com/yechengxi/deconvolution
공식 코드https://github.com/yehli/imagenetmodel
공식 코드https://github.com/yellowmessenger/ood-detection
공식 코드BED: Bi-Encoder-Based Detectors for Out-of-Distribution Detection
https://github.com/yeongjoonju/neuroinspect
공식 코드https://github.com/yeyimilk/llm-vision-marine-animals
공식 코드Benchmarking Large Language Models for Image Classification of Marine Mammals
https://github.com/yformer/EfficientSAM
공식 코드EfficientSAM: Leveraged Masked Image Pretraining for Efficient Segment Anything
https://github.com/ygcats/Scalable-Low-Rank-Compression-for-Neural-Networks
공식 코드Decomposable-Net: Scalable Low-Rank Compression for Neural Networks
https://github.com/ygjwd12345/UCD
공식 코드Uncertainty-aware Contrastive Distillation for Incremental Semantic Segmentation
https://github.com/yhhhli/BRECQ
공식 코드BRECQ: Pushing the Limit of Post-Training Quantization by Block Reconstruction
https://github.com/yhu01/PT-MAP
공식 코드Leveraging the Feature Distribution in Transfer-based Few-Shot Learning
https://github.com/yhu01/bms
공식 코드Squeezing Backbone Feature Distributions to the Max for Efficient Few-Shot Learning
https://github.com/yhu01/transfer-sgc
공식 코드Graph-based Interpolation of Feature Vectors for Accurate Few-Shot Classification
https://github.com/yichen14/fastadasp
공식 코드FastAdaSP: Multitask-Adapted Efficient Inference for Large Speech Language Model
https://github.com/yichen928/activeft
공식 코드Active Finetuning: Exploiting Annotation Budget in the Pretraining-Finetuning Paradigm
https://github.com/yichen928/geal_active_learning
공식 코드https://github.com/yifanzhang-pro/relationmatch
공식 코드RelationMatch: Matching In-batch Relationships for Semi-supervised Learning
https://github.com/yifding/hetseq
공식 코드HetSeq: Distributed GPU Training on Heterogeneous Infrastructure
https://github.com/yigitcankaya/Shallow-Deep-Networks
공식 코드Shallow-Deep Networks: Understanding and Mitigating Network Overthinking
https://github.com/yihongXU/TransCenter
공식 코드TransCenter: Transformers with Dense Representations for Multiple-Object Tracking
https://github.com/yihui-he/Estimated-Depth-Map-Helps-Image-Classification
공식 코드https://github.com/yijinhuang/fpt
공식 코드https://github.com/yikai-wang/knockoffs-spr
공식 코드Knockoffs-SPR: Clean Sample Selection in Learning with Noisy Labels
https://github.com/yikai-wang/spr-lnl
공식 코드Scalable Penalized Regression for Noise Detection in Learning with Noisy Labels
https://github.com/yikun2019/PENCIL
공식 코드Probabilistic End-to-end Noise Correction for Learning with Noisy Labels
https://github.com/yilundu/irem_code_release
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.