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 6176–6200 of 10420 papers

TitleStatusHype
An Analysis of Pre-Training on Object Detection—0
AdaScale SGD: A Scale-Invariant Algorithm for Distributed Training—0
A Class of Short-term Recurrence Anderson Mixing Methods and Their Applications—0
2nd Place Solution for ICCV 2021 VIPriors Image Classification Challenge: An Attract-and-Repulse Learning Approach—0
Maximal Independent Vertex Set applied to Graph Pooling—0
Learning to Find Correlated Features by Maximizing Information Flow in Convolutional Neural Networks—0
Detecting Hypo-plastic Left Heart Syndrome in Fetal Ultrasound via Disease-specific Atlas Maps—0
Learning to Detect Semantic Boundaries with Image-level Class Labels—0
Beyond the Attention: Distinguish the Discriminative and Confusable Features For Fine-grained Image Classification—0
Learning to Detect Malicious Clients for Robust Federated Learning—0
Learning to Detect Blue-white Structures in Dermoscopy Images with Weak Supervision—0
Learning to Complement with Multiple Humans—0
Beyond Size and Class Balance: Alpha as a New Dataset Quality Metric for Deep Learning—0
Learning to combine foveal glimpses with a third-order Boltzmann machine—0
Learning To Collaborate in Decentralized Learning of Personalized Models—0
Learning to Classify New Foods Incrementally Via Compressed Exemplars—0
Detecting Adversaries, yet Faltering to Noise? Leveraging Conditional Variational AutoEncoders for Adversary Detection in the Presence of Noisy Images—0
Learning to be Global Optimizer—0
Mayfly optimization with deep learning enabled retinal fundus image classification model—0
Learning to aggregate feature representations—0
Detecting Adversarial Perturbations with Saliency—0
Efficient Object Embedding for Spliced Image Retrieval—0
Adaptive Weighting Scheme for Automatic Time-Series Data Augmentation—0
Learning to Adapt Category Consistent Meta-Feature of CLIP for Few-Shot Classification—0
Detecting Adversarial Perturbations Through Spatial Behavior in Activation Spaces—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
10RevCol-HTop 1 Accuracy90—Unverified