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 6576–6600 of 10420 papers

TitleStatusHype
Generatively Augmented Neural Network Watchdog for Image Classification Networks—0
A survey on Semi-, Self- and Unsupervised Learning for Image Classification—0
Multi-Instance Learning by Utilizing Structural Relationship among Instances—0
Multi-Instance Multi-Scale CNN for Medical Image Classification—0
Multi-Instance Visual-Semantic Embedding—0
Multi-Label Adversarial Perturbations—0
Dual-Tree Wavelet Packet CNNs for Image Classification—0
Network Architecture Search for Domain Adaptation—0
Generative Data Mining with Longtail-Guided Diffusion—0
Network Augmentation for Tiny Deep Learning—0
Multi-label Image Classification using Adaptive Graph Convolutional Networks: from a Single Domain to Multiple Domains—0
Aggregating Local Deep Features for Image Retrieval—0
Active Learning for Vision-Language Models—0
Multi-Label Image Classification with Regional Latent Semantic Dependencies—0
Multi-Label Image Classification with Contrastive Learning—0
Generative Adversarial Zero-shot Learning via Knowledge Graphs—0
Generative Adversarial Networks for Bitcoin Data Augmentation—0
Generative Adversarial Networks Based on Collaborative Learning and Attention Mechanism for Hyperspectral Image Classification—0
Dynamically Stable Poincaré Embeddings for Neural Manifolds—0
Convex Distillation: Efficient Compression of Deep Networks via Convex Optimization—0
Generative Adversarial Networks Based on Transformer Encoder and Convolution Block for Hyperspectral Image Classification—0
Multi-Label Zero-Shot Learning with Transfer-Aware Label Embedding Projection—0
Multilayer deep feature extraction for visual texture recognition—0
Generative Adversarial Networks and Conditional Random Fields for Hyperspectral Image Classification—0
Converting Transformers to Polynomial Form for Secure Inference Over Homomorphic Encryption—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