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 6851–6875 of 10420 papers

TitleStatusHype
NGC: A Unified Framework for Learning with Open-World Noisy Data—0
Towards Non-I.I.D. Image Classification: A Dataset and Baselines—0
A Survey of Deep Learning for Low-Shot Object Detection—0
Efficient feature embedding of 3D brain MRI images for content-based image retrieval with deep metric learning—0
NIST: An Image Classification Network to Image Semantic Retrieval—0
TransMed: Large Language Models Enhance Vision Transformer for Biomedical Image Classification—0
NN2CAM: Automated Neural Network Mapping for Multi-Precision Edge Processing on FPGA-Based Cameras—0
A Genetic Programming Approach To Zero-Shot Neural Architecture Ranking—0
GCI-ViTAL: Gradual Confidence Improvement with Vision Transformers for Active Learning on Label Noise—0
GCISG: Guided Causal Invariant Learning for Improved Syn-to-real Generalization—0
Efficient Fully Distributed Federated Learning with Adaptive Local Links—0
Noise-Contrastive Variational Information Bottleneck Networks—0
NoiseRank: Unsupervised Label Noise Reduction with Dependence Models—0
Noise-reducing attention cross fusion learning transformer for histological image classification of osteosarcoma—0
An Out-of-the-box Full-network Embedding for Convolutional Neural Networks—0
Noise-Tolerant Few-Shot Unsupervised Adapter for Vision-Language Models—0
To Store or Not? Online Data Selection for Federated Learning with Limited Storage—0
NoisyAG-News: A Benchmark for Addressing Instance-Dependent Noise in Text Classification—0
Centroid Transformers: Learning to Abstract with Attention—0
GC-Fed: Gradient Centralized Federated Learning with Partial Client Participation—0
GCCN: Global Context Convolutional Network—0
Noisy Label Processing for Classification: A Survey—0
How Does a Neural Network's Architecture Impact Its Robustness to Noisy Labels?—0
No more meta-parameter tuning in unsupervised sparse feature learning—0
Contrastive Learning based Hybrid Networks for Long-Tailed Image Classification—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