SOTAVerified

Image Classification

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.

Source: Metamorphic Testing for Object Detection Systems

Papers

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Showing 6926–6950 of 10420 papers

TitleStatusHype
Contrastive Learning of Medical Visual Representations from Paired Images and TextCode1
Transformers: State-of-the-Art Natural Language ProcessingCode0
A new dataset of dog breed images and a benchmark for fine-grained classification—0
Using Unlabeled Data for Increasing Low-Shot Classification Accuracy of Relevant and Open-Set Irrelevant Images—0
MLRSNet: A Multi-label High Spatial Resolution Remote Sensing Dataset for Semantic Scene UnderstandingCode1
High Quality Remote Sensing Image Super-Resolution Using Deep Memory Connected Network—0
Real-time Implementation of RMNv2 Classifier in NXP Bluebox 2.0 and NXP i.MX RT1060—0
Stochastic Precision Ensemble: Self-Knowledge Distillation for Quantized Deep Neural Networks—0
DVERGE: Diversifying Vulnerabilities for Enhanced Robust Generation of EnsemblesCode1
The Utility of Decorrelating Colour Spaces in Vector Quantised Variational AutoencodersCode0
Strategy and Benchmark for Converting Deep Q-Networks to Event-Driven Spiking Neural Networks—0
Attention-Aware Noisy Label Learning for Image Classification—0
Ask-n-Learn: Active Learning via Reliable Gradient Representations for Image Classification—0
A Comparative Study of Deep Learning Loss Functions for Multi-Label Remote Sensing Image Classification—0
MS-RANAS: Multi-Scale Resource-Aware Neural Architecture SearchCode0
Learn like a Pathologist: Curriculum Learning by Annotator Agreement for Histopathology Image Classification—0
Where is the Model Looking At?--Concentrate and Explain the Network Attention—0
Trustworthy Convolutional Neural Networks: A Gradient Penalized-based Approach—0
Attentional Feature FusionCode1
Asymmetric Loss For Multi-Label ClassificationCode1
Predicting the Outputs of Finite Networks Trained with Noisy Gradients—0
Adaptive Risk Minimization: A Meta-Learning Approach for Tackling Group Shift—0
Improving Few-Shot Visual Classification with Unlabelled Examples—0
Long-Tailed Classification by Keeping the Good and Removing the Bad Momentum Causal EffectCode1
Scalable Transfer Learning with Expert Models—0
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All datasetsImageNetCIFAR-10CIFAR-100STL-10ObjectNetMNISTSVHNiNaturalist 2018ImageNet ReaLFlowers-102Clothing1Mmini WebVision 1.0

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CoCa (finetuned)Top 1 Accuracy91—Unverified
2Model soups (BASIC-L)Top 1 Accuracy90.98—Unverified
3Model soups (ViT-G/14)Top 1 Accuracy90.94—Unverified
4DaViT-GTop 1 Accuracy90.4—Unverified
5DaViT-HTop 1 Accuracy90.2—Unverified
6Meta Pseudo Labels (EfficientNet-L2)Top 1 Accuracy90.2—Unverified
7SwinV2-GTop 1 Accuracy90.17—Unverified
8MAWS (ViT-6.5B)Top 1 Accuracy90.1—Unverified
9Florence-CoSwin-HTop 1 Accuracy90.05—Unverified
10RevCol-HTop 1 Accuracy90—Unverified