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

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Showing 4951–5000 of 10420 papers

TitleStatusHype
Augmenting Convolutional networks with attention-based aggregationCode1
PRIME: A few primitives can boost robustness to common corruptionsCode1
A Fistful of Words: Learning Transferable Visual Models from Bag-of-Words Supervision—0
Deep Curriculum Learning for PolSAR Image Classification—0
Virtuoso: Video-based Intelligence for real-time tuning on SOCs—0
ELSA: Enhanced Local Self-Attention for Vision TransformerCode1
Improving Robustness and Uncertainty Modelling in Neural Ordinary Differential Equations—0
Latent Time Neural Ordinary Differential Equations—0
AED: An black-box NLP classifier model attacker—0
Cross-Part Learning for Fine-Grained Image ClassificationCode0
Dynamically Stable Poincaré Embeddings for Neural Manifolds—0
Offloading Algorithms for Maximizing Inference Accuracy on Edge Device Under a Time Constraint—0
RepMLPNet: Hierarchical Vision MLP with Re-parameterized LocalityCode1
Learned Queries for Efficient Local AttentionCode1
A Vision-based Solution for Track Misalignment Detection—0
Encoding Hierarchical Information in Neural Networks helps in Subpopulation Shift—0
Learning with Label Noise for Image Retrieval by Selecting Interactions—0
HarmoFL: Harmonizing Local and Global Drifts in Federated Learning on Heterogeneous Medical ImagesCode1
Transformers Can Do Bayesian InferenceCode1
General Greedy De-bias LearningCode0
Denoised Labels for Financial Time-Series Data via Self-Supervised Learning—0
Zero-shot and Few-shot Learning with Knowledge Graphs: A Comprehensive Survey—0
Interpretable and Interactive Deep Multiple Instance Learning for Dental Caries Classification in Bitewing X-raysCode0
Rank4Class: A Ranking Formulation for Multiclass Classification—0
Soundify: Matching Sound Effects to Video—0
UniMiSS: Universal Medical Self-Supervised Learning via Breaking Dimensionality BarrierCode1
Towards End-to-End Image Compression and Analysis with TransformersCode1
Pixel Distillation: A New Knowledge Distillation Scheme for Low-Resolution Image RecognitionCode1
A Simple Single-Scale Vision Transformer for Object Localization and Instance SegmentationCode0
An Empirical Investigation of the Role of Pre-training in Lifelong LearningCode1
How to augment your ViTs? Consistency loss and StyleAug, a random style transfer augmentation—0
Use Image Clustering to Facilitate Technology Assisted Review—0
Classification of diffraction patterns using a convolutional neural network in single particle imaging experiments performed at X-ray free-electron lasers—0
RegionCLIP: Region-based Language-Image PretrainingCode1
Learning to Prompt for Continual LearningCode1
Pure Noise to the Rescue of Insufficient Data: Improving Imbalanced Classification by Training on Random Noise ImagesCode1
Learning Interpretable Models Through Multi-Objective Neural Architecture SearchCode0
SGML: A Symmetric Graph Metric Learning Framework for Efficient Hyperspectral Image ClassificationCode0
Towards General and Efficient Active LearningCode1
Exploring Category-correlated Feature for Few-shot Image Classification—0
EMDS-6: Environmental Microorganism Image Dataset Sixth Version for Image Denoising, Segmentation, Feature Extraction, Classification and Detection Methods Evaluation—0
Heuristic Hyperparameter Optimization for Convolutional Neural Networks using Genetic AlgorithmCode1
Towards a Unified Foundation Model: Jointly Pre-Training Transformers on Unpaired Images and Text—0
An Interpretive Constrained Linear Model for ResNet and MgNet—0
AdaViT: Adaptive Tokens for Efficient Vision TransformerCode1
WOOD: Wasserstein-based Out-of-Distribution DetectionCode1
Simple and Robust Loss Design for Multi-Label Learning with Missing LabelsCode1
Efficient and Reliable Overlay Networks for Decentralized Federated Learning—0
Magnifying Networks for Images with Billions of Pixels—0
A Discriminative Channel Diversification Network for 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