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 30263050 of 10420 papers

TitleStatusHype
Analysis and Optimization of Loss Functions for Multiclass, Top-k, and Multilabel ClassificationCode0
Blind Image Quality Assessment Using A Deep Bilinear Convolutional Neural NetworkCode0
Directional Label Diffusion Model for Learning from Noisy LabelsCode0
Improving Generalizability of Kolmogorov-Arnold Networks via Error-Correcting Output CodesCode0
Direction Concentration Learning: Enhancing Congruency in Machine LearningCode0
Benchmarking Large Language Models for Image Classification of Marine MammalsCode0
Improving Fairness in Image Classification via SketchingCode0
Improving Generalization and Convergence by Enhancing Implicit RegularizationCode0
An Efficient Framework for Enhancing Discriminative Models via Diffusion TechniquesCode0
Defending Against Physically Realizable Attacks on Image ClassificationCode0
Improving Deep Neural Network Random Initialization Through Neuronal RewiringCode0
Dirty Pixels: Towards End-to-End Image Processing and PerceptionCode0
BlockQNN: Efficient Block-wise Neural Network Architecture GenerationCode0
Improving Ensemble Distillation With Weight Averaging and Diversifying PerturbationCode0
KCNet: An Insect-Inspired Single-Hidden-Layer Neural Network with Randomized Binary Weights for Prediction and Classification TasksCode0
Learning Implicitly Recurrent CNNs Through Parameter SharingCode0
Benchmarking Deep Learning Models on NVIDIA Jetson Nano for Real-Time Systems: An Empirical InvestigationCode0
Towards Difficulty-Agnostic Efficient Transfer Learning for Vision-Language ModelsCode0
Deep Visual City Recognition VisualizationCode0
Adaptive Sample Selection for Robust Learning under Label NoiseCode0
AOGNets: Compositional Grammatical Architectures for Deep LearningCode0
Deep Variation-structured Reinforcement Learning for Visual Relationship and Attribute DetectionCode0
Analysing Training-Data Leakage from Gradients through Linear Systems and Gradient MatchingCode0
Discrete Representations Strengthen Vision Transformer RobustnessCode0
BR-NPA: A Non-Parametric High-Resolution Attention Model to improve the Interpretability of AttentionCode0
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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