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 41514175 of 10420 papers

TitleStatusHype
Using Logic Programming and Kernel-Grouping for Improving Interpretability of Convolutional Neural Networks0
WeedCLR: Weed Contrastive Learning through Visual Representations with Class-Optimized Loss in Long-Tailed Datasets0
Towards Exploring Fairness in Visual Transformer based Natural and GAN Image Detection SystemsCode0
VeRA: Vector-based Random Matrix Adaptation0
United We Stand: Using Epoch-wise Agreement of Ensembles to Combat OverfitCode0
Instilling Inductive Biases with SubnetworksCode0
Relearning Forgotten Knowledge: on Forgetting, Overfit and Training-Free Ensembles of DNNs0
A Non-monotonic Smooth Activation Function0
Soft ascent-descent as a stable and flexible alternative to floodingCode0
A Survey of Graph and Attention Based Hyperspectral Image Classification Methods for Remote Sensing Data0
Transparent Anomaly Detection via Concept-based Explanations0
Prior-Free Continual Learning with Unlabeled Data in the WildCode0
Explore the Effect of Data Selection on Poison Efficiency in Backdoor Attacks0
Plug-and-Play Feature Generation for Few-Shot Medical Image Classification0
Efficient Model-Agnostic Multi-Group Equivariant Networks0
Two Sides of The Same Coin: Bridging Deep Equilibrium Models and Neural ODEs via Homotopy ContinuationCode0
TS-ENAS:Two-Stage Evolution for Cell-based Network Architecture Search0
Subspace Adaptation Prior for Few-Shot LearningCode0
DualAug: Exploiting Additional Heavy Augmentation with OOD Data RejectionCode0
Fusion framework and multimodality for the Laplacian approximation of Bayesian neural networks0
Self-supervised visual learning for analyzing firearms trafficking activities on the Web0
Revisiting Data Augmentation for Rotational Invariance in Convolutional Neural Networks0
Strategies and impact of learning curve estimation for CNN-based image classification0
Does resistance to style-transfer equal Global Shape Bias? Measuring network sensitivity to global shape configurationCode0
NeuroInspect: Interpretable Neuron-based Debugging Framework through Class-conditional VisualizationsCode0
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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