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 28012825 of 10420 papers

TitleStatusHype
NodeFormer: A Scalable Graph Structure Learning Transformer for Node ClassificationCode2
Safeguarding Data in Multimodal AI: A Differentially Private Approach to CLIP TrainingCode0
MOFI: Learning Image Representations from Noisy Entity Annotated ImagesCode1
I See Dead People: Gray-Box Adversarial Attack on Image-To-Text Models0
Learning to Mask and Permute Visual Tokens for Vision Transformer Pre-TrainingCode1
Active Globally Explainable Learning for Medical Images via Class Association Embedding and Cyclic Adversarial Generation0
Scale-Rotation-Equivariant Lie Group Convolution Neural Networks (Lie Group-CNNs)0
Resource Efficient Neural Networks Using Hessian Based Pruning0
Rotational augmentation techniques: a new perspective on ensemble learning for image classification0
Revisiting Token Pruning for Object Detection and Instance SegmentationCode1
Augmenting Zero-Shot Detection Training with Image Labels0
Neural Architecture Design and Robustness: A Dataset0
Computational and Storage Efficient Quadratic Neurons for Deep Neural Networks0
Two-Stage Holistic and Contrastive Explanation of Image ClassificationCode1
Contrastive Learning for Predicting Cancer Prognosis Using Gene Expression ValuesCode0
Hidden Classification Layers: Enhancing linear separability between classes in neural networks layers0
Understanding the Effect of the Long Tail on Neural Network Compression0
FasterViT: Fast Vision Transformers with Hierarchical AttentionCode2
Leveraging Large Language Models for Scalable Vector Graphics-Driven Image UnderstandingCode0
Higher Chest X-ray Resolution Improves Classification Performance0
Large-scale Dataset Pruning with Dynamic UncertaintyCode1
Improving Visual Prompt Tuning for Self-supervised Vision TransformersCode1
Regularizing with Pseudo-Negatives for Continual Self-Supervised LearningCode0
Multi-level Multiple Instance Learning with Transformer for Whole Slide Image ClassificationCode1
LayerAct: Advanced Activation Mechanism for Robust Inference of CNNsCode0
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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