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 6301–6325 of 10420 papers

TitleStatusHype
Beyond Empirical Risk Minimization: Local Structure Preserving Regularization for Improving Adversarial Robustness—0
Designing Adaptive Neural Networks for Energy-Constrained Image Classification—0
Learning on JPEG-LDPC Compressed Images: Classifying with Syndromes—0
Descriptive analysis of computational methods for automating mammograms with practical applications—0
Beyond cross-entropy: learning highly separable feature distributions for robust and accurate classification—0
Analyzing Filters Toward Efficient ConvNet—0
Learning of Time-Frequency Attention Mechanism for Automatic Modulation Recognition—0
Dermoscopic Image Classification with Neural Style Transfer—0
Learning Muti-expert Distribution Calibration for Long-tailed Video Classification—0
MFNet: Multi-class Few-shot Segmentation Network with Pixel-wise Metric Learning—0
Learning Multi-Scale Deep Features for High-Resolution Satellite Image Classification—0
Depthwise-STFT based separable Convolutional Neural Networks—0
Learning multiple non-mutually-exclusive tasks for improved classification of inherently ordered labels—0
Learning Multiple Non-linear Sub-spaces Using K-RBMs—0
Learning Multi-Modal Nonlinear Embeddings: Performance Bounds and an Algorithm—0
Depthwise Separable Convolutions Allow for Fast and Memory-Efficient Spectral Normalization—0
MIAShield: Defending Membership Inference Attacks via Preemptive Exclusion of Members—0
Depthwise Non-local Module for Fast Salient Object Detection Using a Single Thread—0
Analyzing Classifiers: Fisher Vectors and Deep Neural Networks—0
Microscopic fine-grained instance classification through deep attention—0
Adaptive Pixel-wise Structured Sparse Network for Efficient CNNs—0
A Classifier-guided Approach for Top-down Salient Object Detection—0
AHA! an 'Artificial Hippocampal Algorithm' for Episodic Machine Learning—0
Learning More Discriminative Local Descriptors for Few-shot Learning—0
Learning Moderately Input-Sensitive Functions: A Case Study in QR Code Decoding—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