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 11011125 of 10419 papers

TitleStatusHype
Contrastive Losses Are Natural Criteria for Unsupervised Video SummarizationCode1
Contrastive Learning Improves Model Robustness Under Label NoiseCode1
Contrastive Learning of Generalized Game RepresentationsCode1
Contrastive Masked Autoencoders are Stronger Vision LearnersCode1
Convolutional Xformers for VisionCode1
Continual Learning with Scaled Gradient ProjectionCode1
Active Token MixerCode1
ConTNet: Why not use convolution and transformer at the same time?Code1
Continual Learning for LiDAR Semantic Segmentation: Class-Incremental and Coarse-to-Fine strategies on Sparse DataCode1
Continual Hippocampus Segmentation with TransformersCode1
Continual Learning Using a Kernel-Based Method Over Foundation ModelsCode1
Contrasting Intra-Modal and Ranking Cross-Modal Hard Negatives to Enhance Visio-Linguistic Compositional UnderstandingCode1
A graph-transformer for whole slide image classificationCode1
Contextual Transformer Networks for Visual RecognitionCode1
Continual atlas-based segmentation of prostate MRICode1
Content-aware Token Sharing for Efficient Semantic Segmentation with Vision TransformersCode1
AsymmNet: Towards ultralight convolution neural networks using asymmetrical bottlenecksCode1
Asymmetric Polynomial Loss For Multi-Label ClassificationCode1
A Call to Reflect on Evaluation Practices for Failure Detection in Image ClassificationCode1
Contextual Convolutional Neural NetworksCode1
Contrastive Deep SupervisionCode1
Convolution-enhanced Evolving Attention NetworksCode1
Contextual Diversity for Active LearningCode1
Contextual Squeeze-and-Excitation for Efficient Few-Shot Image ClassificationCode1
Benchmarking Pathology Feature Extractors for Whole Slide Image ClassificationCode1
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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