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 90769100 of 10420 papers

TitleStatusHype
Exploring Target Driven Image Classification0
Exploring Temporal Differences in 3D Convolutional Neural Networks0
Exploring the Boundaries of On-Device Inference: When Tiny Falls Short, Go Hierarchical0
Exploring the cloud of feature interaction scores in a Rashomon set0
Exploring the Deep Feature Space of a Cell Classification Neural Network0
Higher Chest X-ray Resolution Improves Classification Performance0
Exploring the Integration of Key-Value Attention Into Pure and Hybrid Transformers for Semantic Segmentation0
Exploring the Sharpened Cosine Similarity0
Exploring the significance of using perceptually relevant image decolorization method for scene classification0
Exploring the Transferability of a Foundation Model for Fundus Images: Application to Hypertensive Retinopathy0
Exploring the Unexplored: Understanding the Impact of Layer Adjustments on Image Classification0
Exploring the Versatility of Zero-Shot CLIP for Interstitial Lung Disease Classification0
Exploring Visual Prompts for Whole Slide Image Classification with Multiple Instance Learning0
Exposing Image Classifier Shortcuts with Counterfactual Frequency (CoF) Tables0
Computational and Storage Efficient Quadratic Neurons for Deep Neural Networks0
Extended Batch Normalization0
Extending Class Activation Mapping Using Gaussian Receptive Field0
Extensions of regret-minimization algorithm for optimal design0
Extracting Human Attention through Crowdsourced Patch Labeling0
Extracurricular Learning: Knowledge Transfer Beyond Empirical Distribution0
Extrapolation Frameworks in Cognitive Psychology Suitable for Study of Image Classification Models0
Extreme Learning Machines for Attention-based Multiple Instance Learning in Whole-Slide Image Classification0
Extreme Network Compression via Filter Group Approximation0
Extreme Sparse Multinomial Logistic Regression: A Fast and Robust Framework for Hyperspectral Image Classification0
Face Clustering: Representation and Pairwise Constraints0
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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