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 7201–7250 of 10420 papers

TitleStatusHype
Effective Label Propagation for Discriminative Semi-Supervised Domain Adaptation—0
Model-Agnostic Learning to Meta-Learn—0
Matching Distributions via Optimal Transport for Semi-Supervised Learning—0
Batch Group Normalization—0
Evolving Character-Level DenseNet Architectures using Genetic Programming—0
Wisdom of Committees: An Overlooked Approach To Faster and More Accurate Models—0
Beyond Cats and Dogs: Semi-supervised Classification of fuzzy labels with overclusteringCode0
Artist, Style And Year Classification Using Face Recognition And Clustering With Convolutional Neural Networks—0
Are Gradient-based Saliency Maps Useful in Deep Reinforcement Learning?—0
Chair Segments: A Compact Benchmark for the Study of Object SegmentationCode0
An Once-for-All Budgeted Pruning Framework for ConvNets Considering Input Resolution—0
A Self-Supervised Feature Map Augmentation (FMA) Loss and Combined Augmentations Finetuning to Efficiently Improve the Robustness of CNNs—0
Rethinking Skip Connection with Layer Normalization—0
Communication-Efficient Federated Distillation—0
Adversarial Robustness Across Representation Spaces—0
Self-Supervised Generative Adversarial Compression—0
One-sample Guided Object Representation Disassembling—0
Model Rubik’s Cube: Twisting Resolution, Depth and Width for TinyNetsCode0
Learning Invariances in Neural Networks from Training Data—0
Pushing the Limits of Narrow Precision Inferencing at Cloud Scale with Microsoft Floating Point—0
A Unified Deep Speaker Embedding Framework for Mixed-Bandwidth Speech Data—0
Generalized Boosting—0
Revisiting Unsupervised Meta-Learning via the Characteristics of Few-Shot TasksCode0
Reducing Textural Bias Improves Robustness of Deep Segmentation Models—0
Scale-covariant and scale-invariant Gaussian derivative networks—0
Context-aware deep model compression for edge cloud computing—0
Class-agnostic Object Detection—0
Unsupervised part representation by Flow Capsules—0
Improving Layer-wise Adaptive Rate Methods using Trust Ratio Clipping—0
torchdistill: A Modular, Configuration-Driven Framework for Knowledge Distillation—0
A Review of Open-World Learning and Steps Toward Open-World Learning Without LabelsCode0
StackMix: A complementary Mix algorithm—0
Grafit: Learning fine-grained image representations with coarse labels—0
Contrastive Representation Learning for Whole Brain Cytoarchitectonic Mapping in Histological Human Brain Sections—0
Applying Convolutional Neural Networks to Data on Unstructured Meshes with Space-Filling CurvesCode0
Benchmarking Inference Performance of Deep Learning Models on Analog Devices—0
Adam^+: A Stochastic Method with Adaptive Variance Reduction—0
A New Periocular Dataset Collected by Mobile Devices in Unconstrained Scenarios—0
Towards Imperceptible Universal Attacks on Texture Recognition—0
Exploring Alternatives to Softmax FunctionCode0
Cancer image classification based on DenseNet model—0
Unsupervised Difficulty Estimation with Action Scores—0
Better Aggregation in Test-Time Augmentation—0
Uncovering the Bias in Facial Expressions—0
Learning Class Unique Features in Fine-Grained Visual Classification—0
Dense open-set recognition with synthetic outliers generated by Real NVPCode0
An Effective Anti-Aliasing Approach for Residual Networks—0
Large Scale Neural Architecture Search with Polyharmonic Splines—0
DeepRepair: Style-Guided Repairing for DNNs in the Real-world Operational Environment—0
On Focal Loss for Class-Posterior Probability Estimation: A Theoretical Perspective—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
10RevCol-HTop 1 Accuracy90—Unverified