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 5326–5350 of 10420 papers

TitleStatusHype
Tiered Pruning for Efficient Differentialble Inference-Aware Neural Architecture Search—0
Robust Collaborative Learning with Linear Gradient OverheadCode0
I2DFormer: Learning Image to Document Attention for Zero-Shot Image Classification—0
PicT: A Slim Weakly Supervised Vision Transformer for Pavement Distress ClassificationCode0
Relaxed Attention for Transformer Models—0
Frequency Dropout: Feature-Level Regularization via Randomized Filtering—0
Fine-grained Classification of Solder Joints with α-skew Jensen-Shannon Divergence—0
On the Adversarial Transferability of ConvMixer Models—0
S^3R: Self-supervised Spectral Regression for Hyperspectral Histopathology Image Classification—0
On the Shift Invariance of Max Pooling Feature Maps in Convolutional Neural Networks—0
Semantic Segmentation using Neural Ordinary Differential Equations—0
Towards Bridging the Performance Gaps of Joint Energy-based ModelsCode0
Enhance the Visual Representation via Discrete Adversarial TrainingCode0
Deep tensor networks with matrix product operators—0
A Mosquito is Worth 16x16 Larvae: Evaluation of Deep Learning Architectures for Mosquito Larvae ClassificationCode0
Continual Learning with Dependency Preserving Hypernetworks—0
Continual Learning for Class- and Domain-Incremental Semantic Segmentation—0
Confidence-Guided Data Augmentation for Improved Semi-Supervised Training—0
Top-Tuning: a study on transfer learning for an efficient alternative to fine tuning for image classification with fast kernel methods—0
A Continual Development Methodology for Large-scale Multitask Dynamic ML SystemsCode0
On the Surprising Effectiveness of Transformers in Low-Labeled Video Recognition—0
Medical Image Segmentation using LeViT-UNet++: A Case Study on GI Tract Data—0
Deep Reinforcement Learning for Task Offloading in UAV-Aided Smart Farm Networks—0
OmniVL:One Foundation Model for Image-Language and Video-Language Tasks—0
PaLI: A Jointly-Scaled Multilingual Language-Image Model—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
5Meta Pseudo Labels (EfficientNet-L2)Top 1 Accuracy90.2—Unverified
6DaViT-HTop 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