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 5576–5600 of 10420 papers

TitleStatusHype
Deep reinforced active learning for multi-class image classification—0
Remote Sensing Image Classification using Transfer Learning and Attention Based Deep Neural Network—0
When Does Re-initialization Work?—0
Using Sum-Product Networks to Assess Uncertainty in Deep Active Learning—0
Out-of-distribution Detection by Cross-class Vicinity Distribution of In-distribution DataCode0
Terrain Classification using Transfer Learning on Hyperspectral Images: A Comparative study—0
0/1 Deep Neural Networks via Block Coordinate Descent—0
Design of Supervision-Scalable Learning Systems: Methodology and Performance Benchmarking—0
Transform-Invariant Convolutional Neural Networks for Image Classification and Search—0
Neural Architecture Adaptation for Object Detection by Searching Channel Dimensions and Mapping Pre-trained Parameters—0
A Comparative Study of Confidence Calibration in Deep Learning: From Computer Vision to Medical Imaging—0
Detecting Adversarial Examples in Batches -- a geometrical approachCode0
The Importance of Background Information for Out of Distribution Generalization—0
Minimum Noticeable Difference based Adversarial Privacy Preserving Image Generation—0
Active Data Discovery: Mining Unknown Data using Submodular Information Measures—0
Open-Set Recognition with Gradient-Based Representations—0
Using adversarial images to improve outcomes of federated learning for non-IID data—0
Efficient Adaptive Ensembling for Image Classification—0
Lessons learned from the NeurIPS 2021 MetaDL challenge: Backbone fine-tuning without episodic meta-learning dominates for few-shot learning image classification—0
Self-Supervised Implicit Attention: Guided Attention by The Model Itself—0
Masked Siamese ConvNets—0
Recent Advances in Scene Image Representation and Classification—0
Self-Supervised Pretraining for Differentially Private LearningCode0
A Survey of Automated Data Augmentation Algorithms for Deep Learning-based Image Classification Tasks—0
Specifying and Testing k-Safety Properties for Machine-Learning ModelsCode0
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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