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 48514875 of 10420 papers

TitleStatusHype
Extensions of regret-minimization algorithm for optimal design0
COMBINED FLEXIBLE ACTIVATION FUNCTIONS FOR DEEP NEURAL NETWORKS0
Extending Class Activation Mapping Using Gaussian Receptive Field0
Are Classification Robustness and Explanation Robustness Really Strongly Correlated? An Analysis Through Input Loss Landscape0
Combined convolutional and recurrent neural networks for hierarchical classification of images0
Extended Batch Normalization0
Computational and Storage Efficient Quadratic Neurons for Deep Neural Networks0
Differentiable Combinatorial Losses through Generalized Gradients of Linear Programs0
Are All Vision Models Created Equal? A Study of the Open-Loop to Closed-Loop Causality Gap0
Adversarial Training for Relation Extraction0
Exposing Image Classifier Shortcuts with Counterfactual Frequency (CoF) Tables0
Exploring Visual Prompts for Whole Slide Image Classification with Multiple Instance Learning0
Combating Noisy Labels in Long-Tailed Image Classification0
Exploring the Versatility of Zero-Shot CLIP for Interstitial Lung Disease Classification0
Exploring the Unexplored: Understanding the Impact of Layer Adjustments on Image Classification0
Exploring the Transferability of a Foundation Model for Fundus Images: Application to Hypertensive Retinopathy0
Exploring the significance of using perceptually relevant image decolorization method for scene classification0
Combating Noise: Semi-supervised Learning by Region Uncertainty Quantification0
Are all negatives created equal in contrastive instance discrimination?0
Adversarial Training for Graph Neural Networks via Graph Subspace Energy Optimization0
A Bag of Visual Words Model for Medical Image Retrieval0
Exploring the Sharpened Cosine Similarity0
Exploring the Integration of Key-Value Attention Into Pure and Hybrid Transformers for Semantic Segmentation0
Color-S^4L: Self-supervised Semi-supervised Learning with Image Colorization0
Higher Chest X-ray Resolution Improves Classification Performance0
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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