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 6651–6700 of 10420 papers

TitleStatusHype
Matching Distributions via Optimal Transport for Semi-Supervised Learning—0
Effective Label Propagation for Discriminative Semi-Supervised Domain Adaptation—0
Model-Agnostic Learning to Meta-Learn—0
Practical No-box Adversarial Attacks against DNNsCode1
Learning Equivariant RepresentationsCode1
Batch Group Normalization—0
Evolving Character-Level DenseNet Architectures using Genetic Programming—0
Robust Instance Segmentation through Reasoning about Multi-Object OcclusionCode1
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
Adversarial Robustness Across Representation Spaces—0
Data Augmentation with norm-VAE for Unsupervised Domain AdaptationCode1
Communication-Efficient Federated Distillation—0
Disentangling Label Distribution for Long-tailed Visual RecognitionCode1
A Unified Deep Speaker Embedding Framework for Mixed-Bandwidth Speech Data—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
Is normalization indispensable for training deep neural network?Code1
Task-Oriented Feature DistillationCode1
HRN: A Holistic Approach to One Class LearningCode1
Co-Tuning for Transfer LearningCode1
Pushing the Limits of Narrow Precision Inferencing at Cloud Scale with Microsoft Floating Point—0
Self-Supervised Generative Adversarial Compression—0
SuperLoss: A Generic Loss for Robust Curriculum LearningCode1
Auto Learning AttentionCode1
Attention-Gated Brain Propagation: How the brain can implement reward-based error backpropagationCode1
Generalized Boosting—0
Towards Better Accuracy-efficiency Trade-offs: Divide and Co-trainingCode1
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
BSNet: Bi-Similarity Network for Few-shot Fine-grained Image ClassificationCode1
ProtoPShare: Prototype Sharing for Interpretable Image Classification and Similarity DiscoveryCode1
Class-agnostic Object Detection—0
General Multi-label Image Classification with TransformersCode1
Unsupervised part representation by Flow Capsules—0
Improving Layer-wise Adaptive Rate Methods using Trust Ratio Clipping—0
Regularization with Latent Space Virtual Adversarial TrainingCode1
How Well Do Self-Supervised Models Transfer?Code1
Grafit: Learning fine-grained image representations with coarse labels—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
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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