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

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Showing 3276–3300 of 10420 papers

TitleStatusHype
Dual Classification Head Self-training Network for Cross-scene Hyperspectral Image Classification—0
Dual Complementary Dynamic Convolution for Image Recognition—0
Android Malware Detection Based on RGB Images and Multi-feature Fusion—0
Extending Class Activation Mapping Using Gaussian Receptive Field—0
DualMamba: A Lightweight Spectral-Spatial Mamba-Convolution Network for Hyperspectral Image Classification—0
Can a face tell us anything about an NBA prospect? -- A Deep Learning approach—0
Dual Path Transformer with Partition Attention—0
BIRD: Behavior Induction via Representation-structure Distillation—0
Extensions of regret-minimization algorithm for optimal design—0
Extracting Human Attention through Crowdsourced Patch Labeling—0
Dual-stream Network for Visual Recognition—0
Differential Privacy Mechanisms in Neural Tangent Kernel Regression—0
Dual Supervised Learning—0
DualToken-ViT: Position-aware Efficient Vision Transformer with Dual Token Fusion—0
Dual-Tree Wavelet Packet CNNs for Image Classification—0
A Closer Look at Prototype Classifier for Few-shot Image Classification—0
1D-Convolutional Capsule Network for Hyperspectral Image Classification—0
Differential Privacy Analysis of Decentralized Gossip Averaging under Varying Threat Models—0
Differentially Private Video Activity Recognition—0
Extracurricular Learning: Knowledge Transfer Beyond Empirical Distribution—0
Differentially Private Meta-Learning—0
Ancient Coin Classification Using Graph Transduction Games—0
Canonical Correlation Analysis of Datasets with a Common Source Graph—0
Dynamic Conditional Networks for Few-Shot Learning—0
Differentially Private Federated Learning via Inexact ADMM with Multiple Local Updates—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