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/cvlab-columbia/ct4recognition
공식 코드https://github.com/cvlab-stonybrook/SelfMedMAE
공식 코드Self Pre-training with Masked Autoencoders for Medical Image Classification and Segmentation
https://github.com/cvlab-stonybrook/ls-sample-quality
공식 코드Assessing Sample Quality via the Latent Space of Generative Models
https://github.com/cvlab-unibo/netspace
공식 코드https://github.com/cvlab-yonsei/EWGS
공식 코드https://github.com/cvmi-lab/simgcd
공식 코드Parametric Classification for Generalized Category Discovery: A Baseline Study
https://github.com/cvmlmu/dgmmc
공식 코드Performance of Gaussian Mixture Model Classifiers on Embedded Feature Spaces
https://github.com/cwangrun/MGProto
공식 코드https://github.com/cwangrun/ST-ProtoPNet
공식 코드Learning Support and Trivial Prototypes for Interpretable Image Classification
https://github.com/cybercore-co-ltd/CoLAD
공식 코드https://github.com/cybercore-co-ltd/colad_paper
공식 코드https://github.com/cydia2018/as-vit
공식 코드Adaptive Sparse ViT: Towards Learnable Adaptive Token Pruning by Fully Exploiting Self-Attention
https://github.com/czczup/FAST
공식 코드FAST: Faster Arbitrarily-Shaped Text Detector with Minimalist Kernel Representation
https://github.com/czgdp1807/pTrustIntervalsOOD
공식 코드Probabilistic Trust Intervals for Out of Distribution Detection
https://github.com/d-becking/efficientCNNs
공식 코드Learning Sparse & Ternary Neural Networks with Entropy-Constrained Trained Ternarization (EC2T)
https://github.com/d-li14/SAN
공식 코드Learning to Learn Parameterized Classification Networks for Scalable Input Images
https://github.com/d-li14/involution
공식 코드Involution: Inverting the Inherence of Convolution for Visual Recognition
https://github.com/dada-bots/dadaGP
공식 코드DadaGP: A Dataset of Tokenized GuitarPro Songs for Sequence Models
https://github.com/dahiyaaneesh/peclr
공식 코드https://github.com/dahyun-kang/mml
공식 코드Memory-Modular Classification: Learning to Generalize with Memory Replacement
https://github.com/dahyun-kang/renet
공식 코드https://github.com/dai-sy-group/dropoutinversionattack
공식 코드https://github.com/daishiresearch/transnext
공식 코드TransNeXt: Robust Foveal Visual Perception for Vision Transformers
https://github.com/damo-cv/elsa
공식 코드https://github.com/damo-cv/entroformer
공식 코드Entroformer: A Transformer-based Entropy Model for Learned Image Compression
https://github.com/damon-demon/black-box-defense
공식 코드How to Robustify Black-Box ML Models? A Zeroth-Order Optimization Perspective
https://github.com/danczs/NetworkAdjustment
공식 코드Network Adjustment: Channel Search Guided by FLOPs Utilization Ratio
https://github.com/danczs/Visformer
공식 코드https://github.com/danfenghong/IEEE_TGRS_GCN
공식 코드Graph Convolutional Networks for Hyperspectral Image Classification
https://github.com/danfenghong/IEEE_TGRS_MDL-RS
공식 코드More Diverse Means Better: Multimodal Deep Learning Meets Remote Sensing Imagery Classification
https://github.com/danfenghong/IEEE_TGRS_SpectralFormer
공식 코드SpectralFormer: Rethinking Hyperspectral Image Classification with Transformers
https://github.com/daniel-ho/SegNBDT
공식 코드https://github.com/danielrossi1/takunet
공식 코드https://github.com/danielshalam/bam
공식 코드Unsupervised Representation Learning by Balanced Self Attention Matching
https://github.com/danielshalam/bpa
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.