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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 6351–6375 of 10420 papers

TitleStatusHype
AHA! an 'Artificial Hippocampal Algorithm' for Episodic Machine Learning—0
Learning More Discriminative Local Descriptors for Few-shot Learning—0
Learning Moderately Input-Sensitive Functions: A Case Study in QR Code Decoding—0
Learning Mid-Level Features and Modeling Neuron Selectivity for Image Classification—0
Learning Mask Invariant Mutual Information for Masked Image Modeling—0
Learning Loss for Test-Time Augmentation—0
Learning Long Sequences in Spiking Neural Networks—0
Learning Longer-term Dependencies in RNNs with Auxiliary Losses—0
Depth Estimation with Simplified Transformer—0
Beyond Batch Learning: Global Awareness Enhanced Domain Adaptation—0
Learning Location from Shared Elevation Profiles in Fitness Apps: A Privacy Perspective—0
MixDefense: A Defense-in-Depth Framework for Adversarial Example Detection Based on Statistical and Semantic Analysis—0
Learning Kernel for Conditional Moment-Matching Discrepancy-based Image Classification—0
Mixed-Precision Quantized Neural Network with Progressively Decreasing Bitwidth For Image Classification and Object Detection—0
DEPICT: Diffusion-Enabled Permutation Importance for Image Classification Tasks—0
Dependency Decomposition and a Reject Option for Explainable Models—0
Learning Invariant Riemannian Geometric Representations Using Deep Nets—0
Mixer: DNN Watermarking using Image Mixup—0
Learning Invariant Representations across Domains and Tasks—0
Density estimation in representation space to predict model uncertainty—0
Beyond ADMM: A Unified Client-variance-reduced Adaptive Federated Learning Framework—0
Estimating the Generalization in Deep Neural Networks via Sparsity—0
Learning Invariances in Neural Networks from Training Data—0
Learning Interpretable Queries for Explainable Image Classification with Information Pursuit—0
Learning Interpretable Logic Rules from Deep Vision Models—0
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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
10Meta Pseudo Labels (EfficientNet-B6-Wide)Top 1 Accuracy90—Unverified