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 2926–2950 of 10420 papers

TitleStatusHype
Admissibility of Stein Shrinkage for Batch Normalization in the Presence of Adversarial Attacks—0
A Distributed Deep Representation Learning Model for Big Image Data Classification—0
C2S2: Cost-aware Channel Sparse Selection for Progressive Network Pruning—0
C2G-Net: Exploiting Morphological Properties for Image Classification—0
A New Window Loss Function for Bone Fracture Detection and Localization in X-ray Images with Point-based Annotation—0
Enhancing Representations through Heterogeneous Self-Supervised Learning—0
Enhancing the Regularization Effect of Weight Pruning in Artificial Neural Networks—0
C2AE: Class Conditioned Auto-Encoder for Open-set Recognition—0
A New Perspective to Boost Vision Transformer for Medical Image Classification—0
Enhancing Neural Training via a Correlated Dynamics Model—0
Byzantines can also Learn from History: Fall of Centered Clipping in Federated Learning—0
Byzantine-Robust and Communication-Efficient Distributed Learning via Compressed Momentum Filtering—0
3DSS-Mamba: 3D-Spectral-Spatial Mamba for Hyperspectral Image Classification—0
Enhancing One-run Privacy Auditing with Quantile Regression-Based Membership Inference—0
Enhancing JEPAs with Spatial Conditioning: Robust and Efficient Representation Learning—0
Bytes Are All You Need: Transformers Operating Directly On File Bytes—0
Enhancing Layer Attention Efficiency through Pruning Redundant Retrievals—0
A Compact Representation of Histopathology Images using Digital Stain Separation & Frequency-Based Encoded Local Projections—0
BUZz: BUffer Zones for defending adversarial examples in image classification—0
Enhancing Intrinsic Features for Debiasing via Investigating Class-Discerning Common Attributes in Bias-Contrastive Pair—0
Enhancing Multimodal In-Context Learning for Image Classification through Coreset Optimization—0
Enhancing Performance of Vision Transformers on Small Datasets through Local Inductive Bias Incorporation—0
Bundle Optimization for Multi-aspect Embedding—0
Building Vision Transformers with Hierarchy Aware Feature Aggregation—0
A New Ensemble Method for Concessively Targeted Multi-model Attack—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
10RevCol-HTop 1 Accuracy90—Unverified