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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/lnsmith54/BOSS
공식 코드Building One-Shot Semi-supervised (BOSS) Learning up to Fully Supervised Performance
https://github.com/locuslab/TCN
공식 코드An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling
https://github.com/locuslab/convmixer
공식 코드https://github.com/locuslab/llava-token-compression
공식 코드Inference Optimal VLMs Need Fewer Visual Tokens and More Parameters
https://github.com/locuslab/mdeq
공식 코드https://github.com/locuslab/projected_sinkhorn
공식 코드Wasserstein Adversarial Examples via Projected Sinkhorn Iterations
https://github.com/locuslab/trellisnet
공식 코드https://github.com/loeweX/Greedy_InfoMax
공식 코드Putting An End to End-to-End: Gradient-Isolated Learning of Representations
https://github.com/log-postech/safe-jax
공식 코드https://github.com/loic-lb/ood_mil
공식 코드On the detection of Out-Of-Distribution samples in Multiple Instance Learning
https://github.com/lolemacs/avagrad
공식 코드https://github.com/lolemacs/soft-sharing
공식 코드Learning Implicitly Recurrent CNNs Through Parameter Sharing
https://github.com/longkukuhi/crisisvit
공식 코드CrisisViT: A Robust Vision Transformer for Crisis Image Classification
https://github.com/longkukuhi/lacvit
공식 코드LaCViT: A Label-aware Contrastive Fine-tuning Framework for Vision Transformers
https://github.com/lorenmt/shape-adaptor
공식 코드https://github.com/lorenzobasile/implicitbiasadversarial
공식 코드Frequency maps reveal the correlation between Adversarial Attacks and Implicit Bias
https://github.com/lorenzobrigato/gem
공식 코드Image Classification with Small Datasets: Overview and Benchmark
https://github.com/lorisnanni/feature-transforms-for-image-data-augmentation
공식 코드https://github.com/loshchil/AdamW-and-SGDW
공식 코드https://github.com/lpd-epfl/robust-collaborative-learning
공식 코드https://github.com/ls1rius/wsi_five
공식 코드Generalizable Whole Slide Image Classification with Fine-Grained Visual-Semantic Interaction
https://github.com/lsl001006/fediod
공식 코드Federated Learning via Input-Output Collaborative Distillation
https://github.com/ltdung/snt-sentry
공식 코드Onboard Satellite Image Classification for Earth Observation: A Comparative Study of ViT Models
https://github.com/lth14/targeted-supcon
공식 코드Targeted Supervised Contrastive Learning for Long-Tailed Recognition
https://github.com/lttm/cl-pcss
공식 코드https://github.com/ltzovo/damamba
공식 코드DAMamba: Vision State Space Model with Dynamic Adaptive Scan
https://github.com/luca-parisi/QReLU_m-QReLU_TensorFlow_Keras
공식 코드QReLU and m-QReLU: Two novel quantum activation functions to aid medical diagnostics
https://github.com/lucasdedieu/noiseresilienthistopathology
공식 코드Contrastive-Based Deep Embeddings for Label Noise-Resilient Histopathology Image Classification
https://github.com/lucasfenaux/pillar-espn
공식 코드Fast and Private Inference of Deep Neural Networks by Co-designing Activation Functions
https://github.com/lucasmansilla/DGvGS
공식 코드https://github.com/lucaspiper99/retinanets-evnets
공식 코드Explicitly Modeling Pre-Cortical Vision with a Neuro-Inspired Front-End Improves CNN Robustness
https://github.com/lujiazho/ssprop
공식 코드https://github.com/lukasc-ch/ExtendedBitPlaneCompression
공식 코드EBPC: Extended Bit-Plane Compression for Deep Neural Network Inference and Training Accelerators
https://github.com/lukasliebel/MultiDepth
공식 코드MultiDepth: Single-Image Depth Estimation via Multi-Task Regression and Classification
https://github.com/lukemelas/do-you-even-need-attention
공식 코드Do You Even Need Attention? A Stack of Feed-Forward Layers Does Surprisingly Well on ImageNet
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