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 7551–7600 of 10420 papers

TitleStatusHype
Instance-based Deep Transfer Learning—0
PRKAN: Parameter-Reduced Kolmogorov-Arnold Networks—0
Instance-Aware Group Quantization for Vision Transformers—0
Deep Learning Approaches for Medical Imaging Under Varying Degrees of Label Availability: A Comprehensive Survey—0
Inspect Transfer Learning Architecture with Dilated Convolution—0
Inspector Gadget: A Data Programming-based Labeling System for Industrial Images—0
INsight: A Neuromorphic Computing System for Evaluation of Large Neural Networks—0
Probabilistic Label Trees for Efficient Large Scale Image Classification—0
InsCon:Instance Consistency Feature Representation via Self-Supervised Learning—0
Probabilistic Model-Based Dynamic Architecture Search—0
Probabilistic Spatial Analysis in Quantitative Microscopy with Uncertainty-Aware Cell Detection using Deep Bayesian Regression of Density Maps—0
Deep Learning Applications Based on WISE Infrared Data: Classification of Stars, Galaxies and Quasars—0
Backdoor Attacks against Image-to-Image Networks—0
Probability Guided Loss for Long-Tailed Multi-Label Image Classification—0
A Methodology to Study the Impact of Spiking Neural Network Parameters considering Event-Based Automotive Data—0
Adaptive Ensemble Learning: Boosting Model Performance through Intelligent Feature Fusion in Deep Neural Networks—0
Deep Learning and Medical Imaging for COVID-19 Diagnosis: A Comprehensive Survey—0
Probing Network Decisions: Capturing Uncertainties and Unveiling Vulnerabilities Without Label Information—0
Inplace knowledge distillation with teacher assistant for improved training of flexible deep neural networks—0
Problem-dependent attention and effort in neural networks with applications to image resolution and model selection—0
Deep Learning and Machine Learning, Advancing Big Data Analytics and Management: Tensorflow Pretrained Models—0
InPK: Infusing Prior Knowledge into Prompt for Vision-Language Models—0
In-Memory Nearest Neighbor Search with FeFET Multi-Bit Content-Addressable Memories—0
Deep learning and hand-crafted features for virus image classification—0
Initializing Perturbations in Multiple Directions for Fast Adversarial Training—0
PROFIT: A Specialized Optimizer for Deep Fine Tuning—0
Initialization Using Perlin Noise for Training Networks with a Limited Amount of Data—0
Deep learning and face recognition: the state of the art—0
Backdoor Attack Detection in Computer Vision by Applying Matrix Factorization on the Weights of Deep Networks—0
Initialization Noise in Image Gradients and Saliency Maps—0
Deep Learning Algorithms with Applications to Video Analytics for A Smart City: A Survey—0
In-Hindsight Quantization Range Estimation for Quantized Training—0
Informed Non-convex Robust Principal Component Analysis with Features—0
Deep Learning Algorithms for Early Diagnosis of Acute Lymphoblastic Leukemia—0
Informative Robust Causal Representation for Generalizable Deep Learning—0
Informative Class Activation Maps—0
Information-theoretical label embeddings for large-scale image classification—0
ProjectionNet: Learning Efficient On-Device Deep Networks Using Neural Projections—0
Projective Skip-Connections for Segmentation Along a Subset of Dimensions in Retinal OCT—0
Deep Kernel Learning via Random Fourier Features—0
BA^2M: A Batch Aware Attention Module for Image Classification—0
A Method for Restoring the Training Set Distribution in an Image Classifier—0
Information Gain Sampling for Active Learning in Medical Image Classification—0
Information contraction in noisy binary neural networks and its implications—0
Joint Device-Edge Inference over Wireless Links with Pruning—0
Prompt-Guided Adaptive Model Transformation for Whole Slide Image Classification—0
Information Bottleneck-Based Hebbian Learning Rule Naturally Ties Working Memory and Synaptic Updates—0
A Zero-shot Learning Method Based on Large Language Models for Multi-modal Knowledge Graph Embedding—0
InfoDisent: Explainability of Image Classification Models by Information Disentanglement—0
Deep Integrated Pipeline of Segmentation Guided Classification of Breast Cancer from Ultrasound Images—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