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

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Showing 9326–9350 of 10420 papers

TitleStatusHype
Comparing LBP, HOG and Deep Features for Classification of Histopathology Images—0
Label Embedding with Partial Heterogeneous Contexts—0
RMDL: Random Multimodel Deep Learning for ClassificationCode0
Structured Analysis Dictionary Learning for Image ClassificationCode0
Unsupervised Learning using Pretrained CNN and Associative Memory Bank—0
Exploring the Limits of Weakly Supervised PretrainingCode0
Augmenting Image Question Answering Dataset by Exploiting Image Captions—0
Incorporating Semantic Attention in Video Description Generation—0
Using Adversarial Examples in Natural Language Processing—0
The Effects of Unimodal Representation Choices on Multimodal Learning—0
Polish Corpus of Annotated Descriptions of Images—0
Sample-to-Sample Correspondence for Unsupervised Domain Adaptation—0
Towards Deeper Generative Architectures for GANs using Dense connections—0
Adversarially Robust Generalization Requires More Data—0
CRAM: Clued Recurrent Attention Model—0
Negative Log Likelihood Ratio Loss for Deep Neural Network Classification—0
IamNN: Iterative and Adaptive Mobile Neural Network for Efficient Image Classification—0
Progressive Neural Networks for Image Classification—0
Anchor-based Nearest Class Mean Loss for Convolutional Neural Networks—0
Study of Residual Networks for Image Recognition—0
Visibility graphs for image processing—0
Randomized ICA and LDA Dimensionality Reduction Methods for Hyperspectral Image Classification—0
Robustness via Deep Low-Rank Representations—0
Co-teaching: Robust Training of Deep Neural Networks with Extremely Noisy LabelsCode1
Neural Compatibility Modeling with Attentive Knowledge Distillation—0
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All datasetsImageNetCIFAR-10CIFAR-100STL-10ObjectNetMNISTSVHNiNaturalist 2018ImageNet ReaLFlowers-102Clothing1Mmini WebVision 1.0

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CoCa (finetuned)Top 1 Accuracy91—Unverified
2Model soups (BASIC-L)Top 1 Accuracy90.98—Unverified
3Model soups (ViT-G/14)Top 1 Accuracy90.94—Unverified
4DaViT-GTop 1 Accuracy90.4—Unverified
5DaViT-HTop 1 Accuracy90.2—Unverified
6Meta Pseudo Labels (EfficientNet-L2)Top 1 Accuracy90.2—Unverified
7SwinV2-GTop 1 Accuracy90.17—Unverified
8MAWS (ViT-6.5B)Top 1 Accuracy90.1—Unverified
9Florence-CoSwin-HTop 1 Accuracy90.05—Unverified
10Meta Pseudo Labels (EfficientNet-B6-Wide)Top 1 Accuracy90—Unverified