Papers with Code 아카이브
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/mevalle/v-nets
공식 코드V-EfficientNets: Vector-Valued Efficiently Scaled Convolutional Neural Network Models
https://github.com/mfauvel/FFFS
공식 코드Fast forward feature selection for the nonlinear classification of hyperspectral images
https://github.com/mfigurnov/sact
공식 코드https://github.com/mgholamikn/ETran
공식 코드https://github.com/miccaiif/dgmil
공식 코드DGMIL: Distribution Guided Multiple Instance Learning for Whole Slide Image Classification
https://github.com/miccaiif/ins
공식 코드https://github.com/miccaiif/openal
공식 코드OpenAL: An Efficient Deep Active Learning Framework for Open-Set Pathology Image Classification
https://github.com/miccaiif/top
공식 코드https://github.com/miccaiif/weno
공식 코드Bi-directional Weakly Supervised Knowledge Distillation for Whole Slide Image Classification
https://github.com/miccunifi/cross-the-gap
공식 코드Cross the Gap: Exposing the Intra-modal Misalignment in CLIP via Modality Inversion
https://github.com/michaelrzhang/lookahead
공식 코드https://github.com/michaelsdr/momentumnet
공식 코드https://github.com/michaelsdr/sinkformers
공식 코드https://github.com/michahu/modeling-training
공식 코드https://github.com/michalkoziarski/DiagSet
공식 코드DiagSet: a dataset for prostate cancer histopathological image classification
https://github.com/michiganleon/reclip_wacv
공식 코드ReCLIP: Refine Contrastive Language Image Pre-Training with Source Free Domain Adaptation
https://github.com/micmic123/qmapcompression
공식 코드Variable-Rate Deep Image Compression through Spatially-Adaptive Feature Transform
https://github.com/microndla/mobilevitv3
공식 코드https://github.com/microsoft/AutoML
공식 코드https://github.com/microsoft/CSWin-Transformer
공식 코드CSWin Transformer: A General Vision Transformer Backbone with Cross-Shaped Windows
https://github.com/microsoft/CvT
공식 코드https://github.com/microsoft/Focal-Transformer
공식 코드Focal Self-attention for Local-Global Interactions in Vision Transformers
https://github.com/microsoft/FocalNet
공식 코드https://github.com/microsoft/GenerativeImage2Text
공식 코드GIT: A Generative Image-to-text Transformer for Vision and Language
https://github.com/microsoft/KC
공식 코드Multi-Level Knowledge Distillation for Out-of-Distribution Detection in Text
https://github.com/microsoft/ORBIT-Dataset
공식 코드ORBIT: A Real-World Few-Shot Dataset for Teachable Object Recognition
https://github.com/microsoft/Relation-Aware-Global-Attention-Networks
공식 코드Relation-Aware Global Attention for Person Re-identification
https://github.com/microsoft/SPACH
공식 코드Sparse MLP for Image Recognition: Is Self-Attention Really Necessary?
https://github.com/microsoft/Swin-Transformer
공식 코드Swin Transformer: Hierarchical Vision Transformer using Shifted Windows
https://github.com/microsoft/activemlp
공식 코드https://github.com/microsoft/blackbox-smoothing
공식 코드Denoised Smoothing: A Provable Defense for Pretrained Classifiers
https://github.com/microsoft/cream
공식 코드Rethinking and Improving Relative Position Encoding for Vision Transformer
https://github.com/microsoft/denoised-smoothing
공식 코드Denoised Smoothing: A Provable Defense for Pretrained Classifiers
https://github.com/microsoft/esvit
공식 코드Efficient Self-supervised Vision Transformers for Representation Learning
https://github.com/microsoft/geo-vision-labeler
공식 코드GeoVision Labeler: Zero-Shot Geospatial Classification with Vision and Language Models
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.