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

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Showing 8051–8100 of 10420 papers

TitleStatusHype
Few-Shot Learning Approach on Tuberculosis Classification Based on Chest X-Ray Images—0
A fast dynamic graph convolutional network and CNN parallel network for hyperspectral image classification—0
Computer vision and machine learning for medical image analysis: recent advances, challenges, and way forward—0
Revisiting Metric Learning for Few-Shot Image Classification—0
Feature Whitening via Gradient Transformation for Improved Convergence—0
Rethinking the Zigzag Flattening for Image Reading—0
Rethinking Two Consensuses of the Transferability in Deep Learning—0
Revisiting MLLMs: An In-Depth Analysis of Image Classification Abilities—0
Rethinking VLMs and LLMs for Image Classification—0
Few-shot Image Classification with Multi-Facet Prototypes—0
Few-Shot Image Classification via Contrastive Self-Supervised Learning—0
Foveated Retinotopy Improves Classification and Localization in CNNs—0
A Self-Supervised Learning Pipeline for Demographically Fair Facial Attribute Classification—0
Compute Less to Get More: Using ORC to Improve Sparse Filtering—0
A Fistful of Words: Learning Transferable Visual Models from Bag-of-Words Supervision—0
Retrieval-enriched zero-shot image classification in low-resource domains—0
Few-shot Image Classification based on Gradual Machine Learning—0
Few-Shot Image Classification and Segmentation as Visual Question Answering Using Vision-Language Models—0
Compute-Efficient Medical Image Classification with Softmax-Free Transformers and Sequence Normalization—0
Few-Shot Image Classification Along Sparse Graphs—0
Reveal of Vision Transformers Robustness against Adversarial Attacks—0
FedDiff: Diffusion Model Driven Federated Learning for Multi-Modal and Multi-Clients—0
Reversed Active Learning based Atrous DenseNet for Pathological Image Classification—0
Reverse engineering adversarial attacks with fingerprints from adversarial examples—0
FedDropoutAvg: Generalizable federated learning for histopathology image classification—0
Unauthorized AI cannot Recognize Me: Reversible Adversarial Example—0
Few-Shot Hyperspectral Image Classification With Unknown Classes Using Multitask Deep Learning—0
Computed tomography using meta-optics—0
A Self-Supervised Feature Map Augmentation (FMA) Loss and Combined Augmentations Finetuning to Efficiently Improve the Robustness of CNNs—0
LAM3D: Leveraging Attention for Monocular 3D Object Detection—0
Review of AlexNet for Medical Image Classification—0
A Test Statistic Estimation-based Approach for Establishing Self-interpretable CNN-based Binary Classifiers—0
Rice Leaf Disease Detection: A Comparative Study Between CNN, Transformer and Non-neural Network Architectures—0
Revisiting Adversarial Risk—0
Revisiting Adversarial Robustness of Classifiers With a Reject Option—0
Revisiting a kNN-based Image Classification System with High-capacity Storage—0
Robust Brain MRI Image Classification with SIBOW-SVM—0
RRR-Net: Reusing, Reducing, and Recycling a Deep Backbone Network—0
Scene Uncertainty and the Wellington Posterior of Deterministic Image Classifiers—0
Revisiting Data Augmentation for Rotational Invariance in Convolutional Neural Networks—0
Federated Learning for Commercial Image Sources—0
The Efficacy of SHIELD under Different Threat Models—0
Few-shot Fine-grained Image Classification via Multi-Frequency Neighborhood and Double-cross ModulationCode0
A self-interpretable module for deep image classification on small dataCode0
Few-Class Arena: A Benchmark for Efficient Selection of Vision Models and Dataset Difficulty MeasurementCode0
Few and Fewer: Learning Better from Few Examples Using Fewer Base ClassesCode0
Feedback Gradient Descent: Efficient and Stable Optimization with Orthogonality for DNNsCode0
Compressing Vision Transformers for Low-Resource Visual LearningCode0
A second-order-like optimizer with adaptive gradient scaling for deep learningCode0
Compressing Deep CNNs using Basis Representation and Spectral Fine-tuningCode0
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All datasetsImageNetCIFAR-10CIFAR-100STL-10ObjectNetMNISTSVHNiNaturalist 2018ImageNet ReaLFlowers-102Clothing1Mmini WebVision 1.0

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CoCa (finetuned)Top 1 Accuracy91—Unverified
2Model soups (BASIC-L)Top 1 Accuracy90.98—Unverified
3Model soups (ViT-G/14)Top 1 Accuracy90.94—Unverified
4DaViT-GTop 1 Accuracy90.4—Unverified
5Meta Pseudo Labels (EfficientNet-L2)Top 1 Accuracy90.2—Unverified
6DaViT-HTop 1 Accuracy90.2—Unverified
7SwinV2-GTop 1 Accuracy90.17—Unverified
8MAWS (ViT-6.5B)Top 1 Accuracy90.1—Unverified
9Florence-CoSwin-HTop 1 Accuracy90.05—Unverified
10RevCol-HTop 1 Accuracy90—Unverified