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 5201–5225 of 10420 papers

TitleStatusHype
Hilbert Sinkhorn Divergence for Optimal Transport—0
Hilbert Curve Based Molecular Sequence Analysis—0
Improving Whole Slide Segmentation Through Visual Context - A Systematic Study—0
High Quality Remote Sensing Image Super-Resolution Using Deep Memory Connected Network—0
Nonlinear Advantage: Trained Networks Might Not Be As Complex as You Think—0
IMWA: Iterative Model Weight Averaging Benefits Class-Imbalanced Learning Tasks—0
Cross Pixel Optical Flow Similarity for Self-Supervised Learning—0
Language-Informed Hyperspectral Image Synthesis for Imbalanced-Small Sample Classification via Semi-Supervised Conditional Diffusion Model—0
High Performance Hyperspectral Image Classification using Graphics Processing Units—0
InceptionCapsule: Inception-Resnet and CapsuleNet with self-attention for medical image Classification—0
High Performance Human Face Recognition using Independent High Intensity Gabor Wavelet Responses: A Statistical Approach—0
CrossoverScheduler: Overlapping Multiple Distributed Training Applications in a Crossover Manner—0
Highly Efficient Representation and Active Learning Framework and Its Application to Imbalanced Medical Image Classification—0
Highly Efficient Forward and Backward Propagation of Convolutional Neural Networks for Pixelwise Classification—0
Incomplete Dot Products for Dynamic Computation Scaling in Neural Network Inference—0
In-context learning enables multimodal large language models to classify cancer pathology images—0
In-Context Learning for Label-Efficient Cancer Image Classification in Oncology—0
Incoporating Weighted Board Learning System for Accurate Occupational Pneumoconiosis Staging—0
AdaFilter: Adaptive Filter Fine-tuning for Deep Transfer Learning—0
Deep FisherNet for Object Classification—0
Incorporating Semantic Attention in Video Description Generation—0
Deep Fisher Networks for Large-Scale Image Classification—0
HIGHLY EFFICIENT 8-BIT LOW PRECISION INFERENCE OF CONVOLUTIONAL NEURAL NETWORKS—0
High Frequency Residual Learning for Multi-Scale Image Classification—0
Cross-Model Consensus of Explanations and Beyond for Image Classification Models: An Empirical Study—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