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 2051–2075 of 10420 papers

TitleStatusHype
Operator-learning-inspired Modeling of Neural Ordinary Differential Equations—0
Semantic-Aware Autoregressive Image Modeling for Visual Representation LearningCode1
SA^2VP: Spatially Aligned-and-Adapted Visual PromptCode1
Catastrophic Forgetting in Deep Learning: A Comprehensive TaxonomyCode4
Transformers in Unsupervised Structure-from-MotionCode1
Improved efficient capsule network for Kuzushiji-MNIST benchmark dataset classificationCode0
Gradient-based Parameter Selection for Efficient Fine-TuningCode1
SkySense: A Multi-Modal Remote Sensing Foundation Model Towards Universal Interpretation for Earth Observation ImageryCode3
A Survey of Classical And Quantum Sequence ModelsCode1
Adversarial Robustness on Image Classification with k-means—0
CLAF: Contrastive Learning with Augmented Features for Imbalanced Semi-Supervised Learning—0
Improving Cross-domain Few-shot Classification with Multilayer PerceptronCode1
Learning Long Sequences in Spiking Neural Networks—0
Weight subcloning: direct initialization of transformers using larger pretrained ones—0
CLIP-guided Federated Learning on Heterogeneous and Long-Tailed DataCode1
Class-Wise Buffer Management for Incremental Object Detection: An Effective Buffer Training Strategy—0
On the Image-Based Detection of Tomato and Corn leaves Diseases : An in-depth comparative experiments—0
Semi-supervised Semantic Segmentation Meets Masked Modeling:Fine-grained Locality Learning Matters in Consistency Regularization—0
Agent Attention: On the Integration of Softmax and Linear AttentionCode2
Contractive error feedback for gradient compression—0
Radio Signal Classification by Adversarially Robust Quantum Machine Learning—0
A Novel Image Classification Framework Based on Variational Quantum Algorithms—0
Robust MRI Reconstruction by Smoothed Unrolling (SMUG)Code0
Building Universal Foundation Models for Medical Image Analysis with Spatially Adaptive NetworksCode1
CLASS-M: Adaptive stain separation-based contrastive learning with pseudo-labeling for histopathological image classificationCode0
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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