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 10511075 of 10419 papers

TitleStatusHype
Thermodynamics-inspired Explanations of Artificial IntelligenceCode1
Compressing Features for Learning with Noisy LabelsCode1
Kernel Attention Transformer (KAT) for Histopathology Whole Slide Image ClassificationCode1
Self-supervised Learning in Remote Sensing: A ReviewCode1
PLATON: Pruning Large Transformer Models with Upper Confidence Bound of Weight ImportanceCode1
Learning Viewpoint-Agnostic Visual Representations by Recovering Tokens in 3D SpaceCode1
Toward Clinically Assisted Colorectal Polyp Recognition via Structured Cross-modal Representation ConsistencyCode1
How to Combine Variational Bayesian Networks in Federated LearningCode1
Feature Re-calibration based Multiple Instance Learning for Whole Slide Image ClassificationCode1
TCJA-SNN: Temporal-Channel Joint Attention for Spiking Neural NetworksCode1
Vicinity Vision TransformerCode1
VulCNN: An Image-inspired Scalable Vulnerability Detection SystemCode1
Contextual Squeeze-and-Excitation for Efficient Few-Shot Image ClassificationCode1
EATFormer: Improving Vision Transformer Inspired by Evolutionary AlgorithmCode1
DGMIL: Distribution Guided Multiple Instance Learning for Whole Slide Image ClassificationCode1
FiT: Parameter Efficient Few-shot Transfer Learning for Personalized and Federated Image ClassificationCode1
Channel Importance Matters in Few-Shot Image ClassificationCode1
Simple and Efficient Architectures for Semantic SegmentationCode1
Boosting the Adversarial Transferability of Surrogate Models with Dark KnowledgeCode1
PRANC: Pseudo RAndom Networks for Compacting deep modelsCode1
Masked Frequency Modeling for Self-Supervised Visual Pre-TrainingCode1
SP-ViT: Learning 2D Spatial Priors for Vision TransformersCode1
Differentiable Top-k Classification LearningCode1
Morphence-2.0: Evasion-Resilient Moving Target Defense Powered by Out-of-Distribution DetectionCode1
Peripheral Vision TransformerCode1
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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