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 6651–6675 of 10420 papers

TitleStatusHype
conv_einsum: A Framework for Representation and Fast Evaluation of Multilinear Operations in Convolutional Tensorial Neural Networks—0
Multiple EffNet/ResNet Architectures for Melanoma Classification—0
Towards Non-I.I.D. Image Classification: A Dataset and Baselines—0
ConvBLS: An Effective and Efficient Incremental Convolutional Broad Learning System for Image Classification—0
A Survey on Evolutionary Computation for Computer Vision and Image Analysis: Past, Present, and Future Trends—0
Wisdom of Committees: An Overlooked Approach To Faster and More Accurate Models—0
Image Segmentation, Compression and Reconstruction from Edge Distribution Estimation with Random Field and Random Cluster Theories—0
Dynamic Probabilistic Pruning: A general framework for hardware-constrained pruning at different granularities—0
NIFF: Alleviating Forgetting in Generalized Few-Shot Object Detection via Neural Instance Feature Forging—0
Multiplex-detection Based Multiple Instance Learning Network for Whole Slide Image Classification—0
Dynamic Routing on Deep Neural Network for Thoracic Disease Classification and Sensitive Area Localization—0
Multiplicative Learning—0
NN2CAM: Automated Neural Network Mapping for Multi-Precision Edge Processing on FPGA-Based Cameras—0
Generating Minimal Adversarial Perturbations with Integrated Adaptive Gradients—0
Multi-Receiver Task-Oriented Communications via Multi-Task Deep Learning—0
Dynamic Scheduling for Vehicle-to-Vehicle Communications Enhanced Federated Learning—0
Control-oriented Clustering of Visual Latent Representation—0
Generating Image Captions in Arabic using Root-Word Based Recurrent Neural Networks and Deep Neural Networks—0
Multi-Sample ζ-mixup: Richer, More Realistic Synthetic Samples from a p-Series Interpolant—0
Dynamic Spectrum Mixer for Visual Recognition—0
A Survey on Deep Learning in Medical Image Analysis—0
A Survey on Deep Learning Hardware Accelerators for Heterogeneous HPC Platforms—0
Generating Efficient DNN-Ensembles with Evolutionary Computation—0
Controllable Invariance through Adversarial Feature Learning—0
Generating Counterfactual Explanations with Natural Language—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