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 1226–1250 of 10420 papers

TitleStatusHype
DynaMixer: A Vision MLP Architecture with Dynamic MixingCode1
Toward Training at ImageNet Scale with Differential PrivacyCode1
An Analysis on Ensemble Learning optimized Medical Image Classification with Deep Convolutional Neural NetworksCode1
Speeding up Heterogeneous Federated Learning with Sequentially Trained SuperclientsCode1
Sphere2Vec: Multi-Scale Representation Learning over a Spherical Surface for Geospatial PredictionsCode1
Convolutional Xformers for VisionCode1
Revisiting Global Pooling through the Lens of Optimal TransportCode1
Adaptive DropBlock Enhanced Generative Adversarial Networks for Hyperspectral Image ClassificationCode1
Revisiting Weakly Supervised Pre-Training of Visual Perception ModelsCode1
PT4AL: Using Self-Supervised Pretext Tasks for Active LearningCode1
It's All in the Head: Representation Knowledge Distillation through Classifier SharingCode1
The CLEAR Benchmark: Continual LEArning on Real-World ImageryCode1
Glance and Focus Networks for Dynamic Visual RecognitionCode1
Robust and Resource-Efficient Data-Free Knowledge Distillation by Generative Pseudo ReplayCode1
BottleFit: Learning Compressed Representations in Deep Neural Networks for Effective and Efficient Split ComputingCode1
Lawin Transformer: Improving Semantic Segmentation Transformer with Multi-Scale Representations via Large Window AttentionCode1
A Conservative Approach for Unbiased Learning on Unknown BiasesCode1
Learnable Lookup Table for Neural Network QuantizationCode1
Learn From Others and Be Yourself in Heterogeneous Federated LearningCode1
Optimal Representations for Covariate ShiftCode1
Finding the Task-Optimal Low-Bit Sub-Distribution in Deep Neural NetworksCode1
A Simple Baseline for Open-Vocabulary Semantic Segmentation with Pre-trained Vision-language ModelCode1
PRIME: A few primitives can boost robustness to common corruptionsCode1
Vision Transformer for Small-Size DatasetsCode1
Augmenting Convolutional networks with attention-based aggregationCode1
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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
5Meta Pseudo Labels (EfficientNet-L2)Top 1 Accuracy90.2—Unverified
6DaViT-HTop 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