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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 7901–7925 of 10420 papers

TitleStatusHype
Reinforcement Explanation Learning—0
Reinforcement Learning Approach to Active Learning for Image Classification—0
Fine-grained Image Classification by Exploring Bipartite-Graph Labels—0
Conditional Consistency Regularization for Semi-Supervised Multi-label Image Classification—0
Reinforcement Learning Platform for Adversarial Black-box Attacks with Custom Distortion Filters—0
Reinforcing Generated Images via Meta-learning for One-Shot Fine-Grained Visual Recognition—0
Fine-Grained Few Shot Learning with Foreground Object Transformation—0
Fine-grained Discriminative Localization via Saliency-guided Faster R-CNN—0
Faster and Accurate Classification for JPEG2000 Compressed Images in Networked Applications—0
Relating Regularization and Generalization through the Intrinsic Dimension of Activations—0
Fine-Grained Classification via Mixture of Deep Convolutional Neural Networks—0
Conditional Graphical Lasso for Multi-Label Image Classification—0
Faster Inference of Integer SWIN Transformer by Removing the GELU Activation—0
WaveMamba: Spatial-Spectral Wavelet Mamba for Hyperspectral Image Classification—0
Fine-grained Classification of Solder Joints with α-skew Jensen-Shannon Divergence—0
Conditional Autoregressors are Interpretable Classifiers—0
Faster Training by Selecting Samples Using Embeddings—0
Relaxed Attention for Transformer Models—0
Fine-graind Image Classification via Combining Vision and Language—0
Relaxing Equivariance Constraints with Non-stationary Continuous Filters—0
Relearning Forgotten Knowledge: on Forgetting, Overfit and Training-Free Ensembles of DNNs—0
Relevance Prediction from Eye-movements Using Semi-interpretable Convolutional Neural Networks—0
Activation by Interval-wise Dropout: A Simple Way to Prevent Neural Networks from Plasticity Loss—0
Relevant-features based Auxiliary Cells for Energy Efficient Detection of Natural Errors—0
Finding Original Image Of A Sub Image Using CNNs—0
Show:102550
← PrevPage 317 of 417Next →
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
10RevCol-HTop 1 Accuracy90—Unverified