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 18011825 of 10420 papers

TitleStatusHype
Anytime Continual Learning for Open Vocabulary ClassificationCode1
FACMIC: Federated Adaptative CLIP Model for Medical Image ClassificationCode1
Adversarial Continual LearningCode1
Failure Detection in Medical Image Classification: A Reality Check and Benchmarking TestbedCode1
Fair Contrastive Learning for Facial Attribute ClassificationCode1
Fair Federated Medical Image Classification Against Quality Shift via Inter-Client Progressive State MatchingCode1
GhostNet: More Features from Cheap OperationsCode1
Generic-to-Specific Distillation of Masked AutoencodersCode1
Fast and Accurate Gigapixel Pathological Image Classification with Hierarchical Distillation Multi-Instance LearningCode1
Fast Hierarchical Games for Image ExplanationsCode1
Optimized spiking neurons classify images with high accuracy through temporal coding with two spikesCode1
Robust Semantic Interpretability: Revisiting Concept Activation VectorsCode1
A Partially Reversible U-Net for Memory-Efficient Volumetric Image SegmentationCode1
Generic Neural Architecture Search via RegressionCode1
Fast and Flexible Multi-Task Classification Using Conditional Neural Adaptive ProcessesCode1
Class-Incremental Grouping Network for Continual Audio-Visual LearningCode1
Fast Convex Optimization for Two-Layer ReLU Networks: Equivalent Model Classes and Cone DecompositionsCode1
Fast AutoAugmentCode1
Faster hyperspectral image classification based on selective kernel mechanism using deep convolutional networksCode1
GenFormer -- Generated Images are All You Need to Improve Robustness of Transformers on Small DatasetsCode1
Faster Meta Update Strategy for Noise-Robust Deep LearningCode1
Collaborative Transformers for Grounded Situation RecognitionCode1
Fast Neural Architecture Search of Compact Semantic Segmentation Models via Auxiliary CellsCode1
FAST: Faster Arbitrarily-Shaped Text Detector with Minimalist Kernel RepresentationCode1
Active Domain Adaptation via Clustering Uncertainty-weighted EmbeddingsCode1
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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
5DaViT-HTop 1 Accuracy90.2Unverified
6Meta Pseudo Labels (EfficientNet-L2)Top 1 Accuracy90.2Unverified
7SwinV2-GTop 1 Accuracy90.17Unverified
8MAWS (ViT-6.5B)Top 1 Accuracy90.1Unverified
9Florence-CoSwin-HTop 1 Accuracy90.05Unverified
10Meta Pseudo Labels (EfficientNet-B6-Wide)Top 1 Accuracy90Unverified