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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/cliffbb/geppy_nn
공식 코드Evolutionary NAS with Gene Expression Programming of Cellular Encoding
https://github.com/clinicalml/seq_to_final_benchmark
공식 코드Seq-to-Final: A Benchmark for Tuning from Sequential Distributions to a Final Time Point
https://github.com/clovaai/AdamP
공식 코드AdamP: Slowing Down the Slowdown for Momentum Optimizers on Scale-invariant Weights
https://github.com/clovaai/CutMix-PyTorch
공식 코드CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Features
https://github.com/clovaai/assembled-cnn
공식 코드Compounding the Performance Improvements of Assembled Techniques in a Convolutional Neural Network
https://github.com/clovaai/donut
공식 코드https://github.com/clovaai/overhaul-distillation
공식 코드https://github.com/clovaai/rexnet
공식 코드https://github.com/clusterformer/clusterformer
공식 코드https://github.com/cmhcbb/Seq2Sick
공식 코드Seq2Sick: Evaluating the Robustness of Sequence-to-Sequence Models with Adversarial Examples
https://github.com/cmsflash/efficient-attention
공식 코드https://github.com/cmu-enyac/Label-Granularity
공식 코드Understanding the Impact of Label Granularity on CNN-based Image Classification
https://github.com/cmu-enyac/VirtualPooling
공식 코드ViP: Virtual Pooling for Accelerating CNN-based Image Classification and Object Detection
https://github.com/cnc-ood/cnc_ood
공식 코드https://github.com/cnguyen10/few_shot_meta_learning
공식 코드Uncertainty in Model-Agnostic Meta-Learning using Variational Inference
https://github.com/cobypenso/noise-aware-conformal-prediction
공식 코드Estimating the Conformal Prediction Threshold from Noisy Labels
https://github.com/cocoalex00/Mamba2D
공식 코드Mamba2D: A Natively Multi-Dimensional State-Space Model for Vision Tasks
https://github.com/cocofeat/eyemost
공식 코드Confidence-aware multi-modality learning for eye disease screening
https://github.com/code-terminator/DilatedRNN
공식 코드https://github.com/codeRimoe/DL_for_RSIs
공식 코드Shorten Spatial-spectral RNN with Parallel-GRU for Hyperspectral Image Classification
https://github.com/coeusguo/ceit
공식 코드https://github.com/cogentlabs/apl
공식 코드Adaptive Posterior Learning: few-shot learning with a surprise-based memory module
https://github.com/comp-syn/comp-syn
공식 코드Divergences in Color Perception between Deep Neural Networks and Humans
https://github.com/compai-lab/2024-miccai-grail-kiechle
공식 코드Graph Neural Networks: A suitable Alternative to MLPs in Latent 3D Medical Image Classification?
https://github.com/compstat-lmu/paper_2019_variationalResampleDistributionShift
공식 코드Variational Resampling Based Assessment of Deep Neural Networks under Distribution Shift
https://github.com/computer-vision-in-the-wild/cvinw_readings
공식 코드ELEVATER: A Benchmark and Toolkit for Evaluating Language-Augmented Visual Models
https://github.com/compvis/disclip
공식 코드Does VLM Classification Benefit from LLM Description Semantics?
https://github.com/congriUQ/physics_aware_surrogate
공식 코드https://github.com/conor-horgan/spectrai
공식 코드https://github.com/corl-team/sdde
공식 코드https://github.com/cornell-zhang/FracBNN
공식 코드FracBNN: Accurate and FPGA-Efficient Binary Neural Networks with Fractional Activations
https://github.com/cps-research-group/cps-ntu-public
공식 코드Demo Abstract: Real-Time Out-of-Distribution Detection on a Mobile Robot
https://github.com/cqu-isse/Plot2API
공식 코드Plot2API: Recommending Graphic API from Plot via Semantic Parsing Guided Neural Network
https://github.com/cradleai/LKS-Dataset
공식 코드SOS: Selective Objective Switch for Rapid Immunofluorescence Whole Slide Image Classification
https://github.com/cranken/cpd
공식 코드Comb, Prune, Distill: Towards Unified Pruning for Vision Model Compression
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.