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

Showing 25012525 of 10420 papers

TitleStatusHype
A Particle Swarm Optimization-based Flexible Convolutional Auto-Encoder for Image ClassificationCode0
Input-gradient space particle inference for neural network ensemblesCode0
Initialization Matters for Adversarial Transfer LearningCode0
Input Invex Neural NetworkCode0
Classifying Textual Data with Pre-trained Vision Models through Transfer Learning and Data TransformationsCode0
A Parameterized Generative Adversarial Network Using Cyclic Projection for Explainable Medical Image ClassificationCode0
Adversarial Defense of Image Classification Using a Variational Auto-EncoderCode0
Adversarial Defense by Suppressing High-frequency ComponentsCode0
Classifying high-dimensional Gaussian mixtures: Where kernel methods fail and neural networks succeedCode0
Inference via Sparse Coding in a Hierarchical Vision ModelCode0
Classifying a specific image region using convolutional nets with an ROI mask as inputCode0
In-domain representation learning for remote sensingCode0
Influence of Image Classification Accuracy on Saliency Map EstimationCode0
Information Competing Process for Learning Diversified RepresentationsCode0
Instance-dependent Label Distribution Estimation for Learning with Label NoiseCode0
Class-Conditioned Transformation for Enhanced Robust Image ClassificationCode0
Revisiting 16-bit Neural Network Training: A Practical Approach for Resource-Limited LearningCode0
Classification-Specific Parts for Improving Fine-Grained Visual CategorizationCode0
Classification robustness to common optical aberrationsCode0
Understanding Intrinsic Robustness Using Label UncertaintyCode0
Increasing-Margin Adversarial (IMA) Training to Improve Adversarial Robustness of Neural NetworksCode0
In-distribution Public Data Synthesis with Diffusion Models for Differentially Private Image ClassificationCode0
Improvising the Learning of Neural Networks on Hyperspherical ManifoldCode0
Inception-inspired LSTM for Next-frame Video PredictionCode0
Improving Transferability of Adversarial Examples with Input DiversityCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CoCa (finetuned)Top 1 Accuracy91Unverified
2Model soups (BASIC-L)Top 1 Accuracy90.98Unverified
3Model soups (ViT-G/14)Top 1 Accuracy90.94Unverified
4DaViT-GTop 1 Accuracy90.4Unverified
5Meta Pseudo Labels (EfficientNet-L2)Top 1 Accuracy90.2Unverified
6DaViT-HTop 1 Accuracy90.2Unverified
7SwinV2-GTop 1 Accuracy90.17Unverified
8MAWS (ViT-6.5B)Top 1 Accuracy90.1Unverified
9Florence-CoSwin-HTop 1 Accuracy90.05Unverified
10RevCol-HTop 1 Accuracy90Unverified