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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 7951–7975 of 10420 papers

TitleStatusHype
Resilient Constrained Learning—0
Fast-ParC: Capturing Position Aware Global Feature for ConvNets and ViTs—0
ResizeMix: Mixing Data with Preserved Object Information and True Labels—0
Enhancing Ship Classification in Optical Satellite Imagery: Integrating Convolutional Block Attention Module with ResNet for Improved Performance—0
Residual CNDS—0
ResidualDroppath: Enhancing Feature Reuse over Residual Connections—0
Representation Memorization for Fast Learning New Knowledge without Forgetting—0
Are Visual Recognition Models Robust to Image Compression?—0
Representation Synthesis by Probabilistic Many-Valued Logic Operation in Self-Supervised Learning—0
How Good is your Explanation? Algorithmic Stability Measures to Assess the Quality of Explanations for Deep Neural Networks—0
Filter Distribution Templates in Convolutional Networks for Image Classification Tasks—0
REP: Resource-Efficient Prompting for Rehearsal-Free Continual Learning—0
A Foreground Inference Network for Video Surveillance Using Multi-View Receptive Field—0
Residual Error: a New Performance Measure for Adversarial Robustness—0
FILM: How can Few-Shot Image Classification Benefit from Pre-Trained Language Models?—0
Concurrent Neural Tree and Data Preprocessing AutoML for Image Classification—0
Fighting Fire with Fire: Avoiding DNN Shortcuts through Priming—0
Concurrent Classifier Error Detection (CCED) in Large Scale Machine Learning Systems—0
A Simple and Generic Framework for Feature Distillation via Channel-wise Transformation—0
Resampled Datasets Are Not Enough: Mitigating Societal Bias Beyond Single Attributes—0
Residual and Attentional Architectures for Vector-Symbols—0
Residual Feature-Reutilization Inception Network for Image Classification—0
ResBit: Residual Bit Vector for Categorical Values—0
ResBuilder: Automated Learning of Depth with Residual Structures—0
FickleNet: Weakly and Semi-supervised Semantic Image Segmentation using Stochastic Inference—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
10Meta Pseudo Labels (EfficientNet-B6-Wide)Top 1 Accuracy90—Unverified