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 21512175 of 10420 papers

TitleStatusHype
Continual atlas-based segmentation of prostate MRICode1
EGC: Image Generation and Classification via a Diffusion Energy-Based ModelCode1
Contrasting Intra-Modal and Ranking Cross-Modal Hard Negatives to Enhance Visio-Linguistic Compositional UnderstandingCode1
ELSA: Enhanced Local Self-Attention for Vision TransformerCode1
A General Framework For Detecting Anomalous Inputs to DNN ClassifiersCode1
Continual Hippocampus Segmentation with TransformersCode1
Concept Learners for Few-Shot LearningCode1
Enabling Deep Spiking Neural Networks with Hybrid Conversion and Spike Timing Dependent BackpropagationCode1
Continual Learning for LiDAR Semantic Segmentation: Class-Incremental and Coarse-to-Fine strategies on Sparse DataCode1
Engineering flexible machine learning systems by traversing functionally-invariant pathsCode1
Enhance Image Classification via Inter-Class Image Mixup with Diffusion ModelCode1
Enhancing CLIP with CLIP: Exploring Pseudolabeling for Limited-Label Prompt TuningCode1
Continual Learning Using a Kernel-Based Method Over Foundation ModelsCode1
Enhancing Sharpness-Aware Optimization Through Variance SuppressionCode1
EntAugment: Entropy-Driven Adaptive Data Augmentation Framework for Image ClassificationCode1
Entroformer: A Transformer-based Entropy Model for Learned Image CompressionCode1
Detecting AutoAttack Perturbations in the Frequency DomainCode1
Convolutional Sequence to Sequence LearningCode1
Continual Learning with Scaled Gradient ProjectionCode1
ERNIE-Layout: Layout Knowledge Enhanced Pre-training for Visually-rich Document UnderstandingCode1
Escaping the Big Data Paradigm with Compact TransformersCode1
ESPT: A Self-Supervised Episodic Spatial Pretext Task for Improving Few-Shot LearningCode1
Evaluating histopathology transfer learning with ChampKitCode1
Towards a More Rigorous Science of Blindspot Discovery in Image Classification ModelsCode1
Designing Network Design SpacesCode1
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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