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 5026–5050 of 10420 papers

TitleStatusHype
Tiny Updater: Towards Efficient Neural Network-Driven Software UpdatingCode0
Quantum-Inspired Spectral-Spatial Pyramid Network for Hyperspectral Image Classification—0
Chest X-Ray Images Classification with CNNCode0
DiRaC-I: Identifying Diverse and Rare Training Classes for Zero-Shot Learning—0
TransIFC: Invariant Cues-aware Feature Concentration Learning for Efficient Fine-grained Bird Image Classification—0
Machine Learning and Thermography Applied to the Detection and Classification of Cracks in Building—0
On Learning the Structure of Clusters in Graphs—0
Thermal Heating in ReRAM Crossbar Arrays: Challenges and Solutions—0
Sparse Mixture Once-for-all Adversarial Training for Efficient In-Situ Trade-Off Between Accuracy and Robustness of DNNs—0
Langevin algorithms for very deep Neural Networks with application to image classificationCode0
Attribute-Guided Multi-Level Attention Network for Fine-Grained Fashion RetrievalCode0
Saliency-Augmented Memory Completion for Continual LearningCode0
Hyperspherical Loss-Aware Ternary Quantization—0
LMFLOSS: A Hybrid Loss For Imbalanced Medical Image ClassificationCode0
When are Lemons Purple? The Concept Association Bias of Vision-Language Models—0
Understanding and Improving the Role of Projection Head in Self-Supervised Learning—0
Class Prototype-based Cleaner for Label Noise LearningCode0
Decision-making and control with diffractive optical networksCode0
Calibrating Deep Neural Networks using Explicit Regularisation and Dynamic Data Pruning—0
Temporal Output Discrepancy for Loss Estimation-based Active Learning—0
DDIPNet and DDIPNet+: Discriminant Deep Image Prior Networks for Remote Sensing Image Classification—0
Galaxy Image Classification using Hierarchical Data Learning with Weighted Sampling and Label SmoothingCode0
Unified Framework for Histopathology Image Augmentation and Classification via Generative Models—0
Improving Pre-Trained Weights Through Meta-Heuristics Fine-TuningCode0
Rethinking Label Smoothing on Multi-hop Question AnsweringCode0
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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
10RevCol-HTop 1 Accuracy90—Unverified