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 4076–4100 of 10420 papers

TitleStatusHype
Designing Adaptive Neural Networks for Energy-Constrained Image Classification—0
Descriptive analysis of computational methods for automating mammograms with practical applications—0
Feedback Control for Online Training of Neural Networks—0
Beyond cross-entropy: learning highly separable feature distributions for robust and accurate classification—0
Analyzing Filters Toward Efficient ConvNet—0
FENAS: Flexible and Expressive Neural Architecture Search—0
FermiNets: Learning generative machines to generate efficient neural networks via generative synthesis—0
Dermoscopic Image Classification with Neural Style Transfer—0
FETCH: A Memory-Efficient Replay Approach for Continual Learning in Image Classification—0
Compressively Sensed Image Recognition—0
AFINet: Attentive Feature Integration Networks for Image Classification—0
Depthwise-STFT based separable Convolutional Neural Networks—0
Depthwise Separable Convolutions Allow for Fast and Memory-Efficient Spectral Normalization—0
Depthwise Non-local Module for Fast Salient Object Detection Using a Single Thread—0
Few-shot Algorithm Assurance—0
Analyzing Classifiers: Fisher Vectors and Deep Neural Networks—0
Few-shot crack image classification using clip based on bayesian optimization—0
Computation and Communication Efficient Lightweighting Vertical Federated Learning for Smart Building IoT—0
Few-Shot Histopathology Image Classification: Evaluating State-of-the-Art Methods and Unveiling Performance Insights—0
Adaptive Pixel-wise Structured Sparse Network for Efficient CNNs—0
Half-CNN: A General Framework for Whole-Image Regression—0
Depth Estimation with Simplified Transformer—0
Beyond Batch Learning: Global Awareness Enhanced Domain Adaptation—0
Estimating the Generalization in Deep Neural Networks via Sparsity—0
Beyond ADMM: A Unified Client-variance-reduced Adaptive Federated Learning Framework—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
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