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 17011725 of 10420 papers

TitleStatusHype
Efficient-CapsNet: Capsule Network with Self-Attention RoutingCode1
Meta Adversarial Training against Universal PatchesCode1
Generative Multi-Label Zero-Shot LearningCode1
Advantages and Bottlenecks of Quantum Machine Learning for Remote SensingCode1
Malware Detection Using Frequency Domain-Based Image Visualization and Deep LearningCode1
Online Continual Learning in Image Classification: An Empirical SurveyCode1
DAF:re: A Challenging, Crowd-Sourced, Large-Scale, Long-Tailed Dataset For Anime Character RecognitionCode1
Analysis and evaluation of Deep Learning based Super-Resolution algorithms to improve performance in Low-Resolution Face RecognitionCode1
HarDNet-MSEG: A Simple Encoder-Decoder Polyp Segmentation Neural Network that Achieves over 0.9 Mean Dice and 86 FPSCode1
Counterfactual Generative NetworksCode1
Hyperspectral Image Classification-Traditional to Deep Models: A Survey for Future ProspectsCode1
Attention-Based Second-Order Pooling Network for Hyperspectral Image ClassificationCode1
Big Self-Supervised Models Advance Medical Image ClassificationCode1
Re-labeling ImageNet: from Single to Multi-Labels, from Global to Localized LabelsCode1
Robustness of on-device Models: Adversarial Attack to Deep Learning Models on Android AppsCode1
Mixup Without HesitationCode1
Quantum Tensor Network in Machine Learning: An Application to Tiny Object ClassificationCode1
Few-shot Image Classification: Just Use a Library of Pre-trained Feature Extractors and a Simple ClassifierCode1
Improve Object Detection with Feature-based Knowledge Distillation: Towards Accurate and Efficient DetectorsCode1
Co2L: Contrastive Continual LearningCode1
Iranis: A Large-scale Dataset of Farsi License Plate CharactersCode1
Recall Loss for Imbalanced Image Classification and Semantic SegmentationCode1
Kaleidoscope: An Efficient, Learnable Representation For All Structured Linear MapsCode1
Attention-Based Adaptive Spectral-Spatial Kernel ResNet for Hyperspectral Image ClassificationCode1
Deep Semantic Dictionary Learning for Multi-label 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