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 91269150 of 10420 papers

TitleStatusHype
PCDAL: A Perturbation Consistency-Driven Active Learning Approach for Medical Image Segmentation and ClassificationCode0
Local Relation Networks for Image RecognitionCode0
Dirty Pixels: Towards End-to-End Image Processing and PerceptionCode0
Improving Generalizability of Kolmogorov-Arnold Networks via Error-Correcting Output CodesCode0
Improving Generalization and Convergence by Enhancing Implicit RegularizationCode0
Direct side information learning for zero-shot regressionCode0
Improving Generalization of Batch Whitening by Convolutional Unit OptimizationCode0
A Bayesian Evaluation Framework for Subjectively Annotated Visual Recognition TasksCode0
Multiclass Wound Image Classification using an Ensemble Deep CNN-based ClassifierCode0
A Signal Propagation Perspective for Pruning Neural Networks at InitializationCode0
Multi-column Deep Neural Networks for Image ClassificationCode0
A Separable Self-attention Inspired by the State Space Model for Computer VisionCode0
SynerMix: Synergistic Mixup Solution for Enhanced Intra-Class Cohesion and Inter-Class Separability in Image ClassificationCode0
Certification for Differentially Private Prediction in Gradient-Based TrainingCode0
Adaptive hybrid activation function for deep neural networksCode0
Adaptive feature recombination and recalibration for semantic segmentation with Fully Convolutional NetworksCode0
Center Smoothing: Certified Robustness for Networks with Structured OutputsCode0
Cells are Actors: Social Network Analysis with Classical ML for SOTA Histology Image ClassificationCode0
Improving Intervention Efficacy via Concept Realignment in Concept Bottleneck ModelsCode0
Improving k-Means Clustering Performance with Disentangled Internal RepresentationsCode0
Adaptive Cross-Modal Few-Shot LearningCode0
MultiDepth: Single-Image Depth Estimation via Multi-Task Regression and ClassificationCode0
Improving Long-tailed Object Detection with Image-Level Supervision by Multi-Task Collaborative LearningCode0
Multi-digit Number Recognition from Street View Imagery using Deep Convolutional Neural NetworksCode0
Direction Concentration Learning: Enhancing Congruency in Machine LearningCode0
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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
5DaViT-HTop 1 Accuracy90.2Unverified
6Meta Pseudo Labels (EfficientNet-L2)Top 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