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 50515075 of 10420 papers

TitleStatusHype
CondenseNeXt: An Ultra-Efficient Deep Neural Network for Embedded SystemsCode0
A Mathematical Framework for Quantifying Transferability in Multi-source Transfer Learning0
Focal Attention for Long-Range Interactions in Vision TransformersCode1
argmax centroid0
Adversarial Teacher-Student Representation Learning for Domain GeneralizationCode0
Explanation-based Data Augmentation for Image ClassificationCode0
Revitalizing CNN Attention via Transformers in Self-Supervised Visual Representation LearningCode1
Memory-efficient Patch-based Inference for Tiny Deep Learning0
Pooling by Sliced-Wasserstein EmbeddingCode1
Representer Point Selection via Local Jacobian Expansion for Post-hoc Classifier Explanation of Deep Neural Networks and Ensemble ModelsCode0
Differentiable Spike: Rethinking Gradient-Descent for Training Spiking Neural Networks0
Bayesian Adaptation for Covariate Shift0
Looking Beyond Single Images for Contrastive Semantic Segmentation Learning0
Local Signal Adaptivity: Provable Feature Learning in Neural Networks Beyond KernelsCode0
I-CNet: Leveraging Involution and Convolution for Image Classification0
Sound-Guided Semantic Image ManipulationCode1
MapReader: A Computer Vision Pipeline for the Semantic Exploration of Maps at ScaleCode1
MC-SSL0.0: Towards Multi-Concept Self-Supervised Learning0
Pyramid Adversarial Training Improves ViT PerformanceCode0
Boosting Discriminative Visual Representation Learning with Scenario-Agnostic Mixup0
Human Imperceptible Attacks and Applications to Improve Fairness0
Adaptive Token Sampling For Efficient Vision TransformersCode1
The Devil is in the Margin: Margin-based Label Smoothing for Network CalibrationCode1
Anomaly-Aware Semantic Segmentation by Leveraging Synthetic-Unknown Data0
Learning with Noisy Labels by Efficient Transition Matrix Estimation to Combat Label MiscorrectionCode1
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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