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 84268450 of 10420 papers

TitleStatusHype
Unsupervised Feature Learning with K-means and An Ensemble of Deep Convolutional Neural Networks for Medical Image Classification0
Learning Representations of Graph Data -- A Survey0
Towards Non-I.I.D. Image Classification: A Dataset and Baselines0
Variational Resampling Based Assessment of Deep Neural Networks under Distribution ShiftCode0
Adversarial Explanations for Understanding Image Classification Decisions and Improved Neural Network RobustnessCode0
Iterative Self-Learning: Semi-Supervised Improvement to Dataset Volumes and Model Accuracy0
Playing the lottery with rewards and multiple languages: lottery tickets in RL and NLP0
Robust Attacks against Multiple ClassifiersCode0
StyleNAS: An Empirical Study of Neural Architecture Search to Uncover Surprisingly Fast End-to-End Universal Style Transfer Networks0
Should Adversarial Attacks Use Pixel p-Norm?0
When Does Label Smoothing Help?Code1
Bad Global Minima Exist and SGD Can Reach ThemCode0
Collage Inference: Achieving low tail latency during distributed image classification using coded redundancy models0
PI-Net: A Deep Learning Approach to Extract Topological Persistence ImagesCode0
c-Eval: A Unified Metric to Evaluate Feature-based Explanations via Perturbation0
Multi-way Encoding for Robustness0
Visual Confusion Label Tree For Image Classification0
Visual Tree Convolutional Neural Network in Image Classification0
Geo-Aware Networks for Fine-Grained RecognitionCode0
An Introduction to Deep Morphological Networks0
Information Competing Process for Learning Diversified RepresentationsCode0
Constructing Energy-efficient Mixed-precision Neural Networks through Principal Component Analysis for Edge IntelligenceCode0
Embedded hyper-parameter tuning by Simulated AnnealingCode0
Terminal Brain Damage: Exposing the Graceless Degradation in Deep Neural Networks Under Hardware Fault Attacks0
Learning Representations by Maximizing Mutual Information Across ViewsCode0
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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
10Meta Pseudo Labels (EfficientNet-B6-Wide)Top 1 Accuracy90Unverified