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 451–475 of 10419 papers

TitleStatusHype
Contrastive Forward-Forward: A Training Algorithm of Vision Transformer—0
Redefining Machine Unlearning: A Conformal Prediction-Motivated Approach—0
An All-digital 8.6-nJ/Frame 65-nm Tsetlin Machine Image Classification AcceleratorCode0
Fairness Analysis of CLIP-Based Foundation Models for X-Ray Image Classification—0
OT-Transformer: A Continuous-time Transformer Architecture with Optimal Transport Regularization—0
DebugAgent: Efficient and Interpretable Error Slice Discovery for Comprehensive Model Debugging—0
Toward Relative Positional Encoding in Spiking Transformers—0
Improving Interpretability and Accuracy in Neuro-Symbolic Rule Extraction Using Class-Specific Sparse Filters—0
Generating customized prompts for Zero-Shot Rare Event Medical Image Classification using LLMCode0
SPECIAL: Zero-shot Hyperspectral Image Classification With CLIPCode1
The Linear Attention Resurrection in Vision Transformer—0
Enhancing the Convergence of Federated Learning Aggregation Strategies with Limited Data—0
Building Efficient Lightweight CNN Models—0
Fuzzy-aware Loss for Source-free Domain Adaptation in Visual Emotion Recognition—0
Pre-trained Model Guided Mixture Knowledge Distillation for Adversarial Federated Learning—0
Feasible LearningCode0
Rethinking Foundation Models for Medical Image Classification through a Benchmark Study on MedMNIST—0
SpikePack: Enhanced Information Flow in Spiking Neural Networks with High Hardware Compatibility—0
TLXML: Task-Level Explanation of Meta-Learning via Influence Functions—0
Geometric Mean Improves Loss For Few-Shot Learning—0
Relative Layer-Wise Relevance Propagation: a more Robust Neural Networks eXplaination—0
Impact of Batch Normalization on Convolutional Network Representations—0
Correlation-Based Band Selection for Hyperspectral Image ClassificationCode0
Attribute-based Visual Reprogramming for Image Classification with CLIPCode0
Reinforcement Learning Platform for Adversarial Black-box Attacks with Custom Distortion Filters—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