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 7151–7175 of 10420 papers

TitleStatusHype
Fusing CNNs and statistical indicators to improve image classificationCode0
Multi-Label Noise Robust Collaborative Learning for Remote Sensing Image ClassificationCode0
Augmentation Inside the Network—0
Minimax Active Learning—0
RAILS: A Robust Adversarial Immune-inspired Learning System—0
Separation and Concentration in Deep NetworksCode0
Enabling Retrain-free Deep Neural Network Pruning using Surrogate Lagrangian Relaxation—0
Learning and Sharing: A Multitask Genetic Programming Approach to Image Feature Learning—0
Attention-based Image Upsampling—0
Difficulty in estimating visual information from randomly sampled images—0
Deep Learning of Cell Classification using Microscope Images of Intracellular Microtubule Networks—0
mDALU: Multi-Source Domain Adaptation and Label Unification with Partial Datasets—0
CosSGD: Communication-Efficient Federated Learning with a Simple Cosine-Based Quantization—0
Convolutional Neural Networks from Image Markers—0
Combining Similarity and Adversarial Learning to Generate Visual Explanation: Application to Medical Image Classification—0
Application of the Neural Network Dependability Kit in Real-World Environments—0
DSM Refinement with Deep Encoder-Decoder Networks—0
Improving model calibration with accuracy versus uncertainty optimizationCode0
Graphs for deep learning representations—0
Aggregative Self-Supervised Feature Learning from a Limited Sample—0
Privacy-preserving Decentralized Aggregation for Federated Learning—0
Delay Differential Neural Networks—0
Learning Consistent Deep Generative Models from Sparse Data via Prediction Constraints—0
Assessing The Importance Of Colours For CNNs In Object Recognition—0
Dependency Decomposition and a Reject Option for Explainable Models—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
10RevCol-HTop 1 Accuracy90—Unverified