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 6826–6850 of 10420 papers

TitleStatusHype
Neural Networks Designing Neural Networks: Multi-Objective Hyper-Parameter Optimization—0
Language-Informed Hyperspectral Image Synthesis for Imbalanced-Small Sample Classification via Semi-Supervised Conditional Diffusion Model—0
Deep Self-taught Learning for Remote Sensing Image Classification—0
Deep Self-Learning From Noisy Labels—0
Language-aware Domain Generalization Network for Cross-Scene Hyperspectral Image Classification—0
Landslide4Sense: Reference Benchmark Data and Deep Learning Models for Landslide Detection—0
Deep Selector-JPEG: Adaptive JPEG Image Compression for Computer Vision in Image classification with Human Vision Criteria—0
Landscape of Neural Architecture Search across sensors: how much do they differ ?—0
Land Cover Classification from Multi-temporal, Multi-spectral Remotely Sensed Imagery using Patch-Based Recurrent Neural Networks—0
LANCE: Efficient Low-Precision Quantized Winograd Convolution for Neural Networks Based on Graphics Processing Units—0
Ladder Networks for Semi-Supervised Hyperspectral Image Classification—0
Deep Scene Image Classification With the MFAFVNet—0
NeurIPS'22 Cross-Domain MetaDL competition: Design and baseline results—0
Bayesian Test-Time Adaptation for Vision-Language Models—0
LaCoOT: Layer Collapse through Optimal Transport—0
Laconic Image Classification: Human vs. Machine Performance—0
Laconic Deep Learning Computing—0
LABO: Towards Learning Optimal Label Regularization via Bi-level Optimization—0
Neurosymbolic hybrid approach to driver collision warning—0
Revisiting the Role of Label Smoothing in Enhanced Text Sentiment Classification—0
Label-similarity Curriculum Learning—0
Deep Residual Network based food recognition for enhanced Augmented Reality application—0
Label-noise-tolerant medical image classification via self-attention and self-supervised learning—0
Label Geometry Aware Discriminator for Conditional Generative Networks—0
Label Embedding with Partial Heterogeneous Contexts—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