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 9901–9950 of 10420 papers

TitleStatusHype
Instance-Aware Group Quantization for Vision Transformers—0
Instance-based Deep Transfer Learning—0
Instant Adversarial Purification with Adversarial Consistency Distillation—0
Institutionally Distributed Deep Learning Networks—0
Instruction-ViT: Multi-Modal Prompts for Instruction Learning in ViT—0
Integrated perception with recurrent multi-task neural networks—0
GeoDiffusion: Text-Prompted Geometric Control for Object Detection Data Generation—0
Integrating Propositional and Relational Label Side Information for Hierarchical Zero-Shot Image Classification—0
Integrating Scene Text and Visual Appearance for Fine-Grained Image Classification—0
Integration and Performance Analysis of Artificial Intelligence and Computer Vision Based on Deep Learning Algorithms—0
Integration of Roadside Camera Images and Weather Data for Monitoring Winter Road Surface Conditions—0
Intelligent Cervical Spine Fracture Detection Using Deep Learning Methods—0
Intelligent Known and Novel Aircraft Recognition -- A Shift from Classification to Similarity Learning for Combat Identification—0
Interaction as Explanation: A User Interaction-based Method for Explaining Image Classification Models—0
Interactive Knowledge Distillation—0
Interactive Visual Study of Multiple Attributes Learning Model of X-Ray Scattering Images—0
IC-Network: Efficient Structure for Convolutional Neural Networks—0
Interleaving Learning, with Application to Neural Architecture Search—0
Interpolation between CNNs and ResNets—0
Interpretable Bilingual Multimodal Large Language Model for Diverse Biomedical Tasks—0
Interpretable Deep Models for Cardiac Resynchronisation Therapy Response Prediction—0
Interpretable Failure Detection with Human-Level Concepts—0
Interpretable Few-Shot Image Classification via Prototypical Concept-Guided Mixture of LoRA Experts—0
Interpretable Mammographic Image Classification using Case-Based Reasoning and Deep Learning—0
Interpreting Bias in the Neural Networks: A Peek Into Representational Similarity—0
Interpreting Equivariant Representations—0
Interpreting Interpretations: Organizing Attribution Methods by Criteria—0
Interpreting the Predictions of Complex ML Models by Layer-wise Relevance Propagation—0
Interpreting Undesirable Pixels for Image Classification on Black-Box Models—0
Interventional Black-Box Explanations—0
Intra-Model Collaborative Learning of Neural Networks—0
Intriguing Frequency Interpretation of Adversarial Robustness for CNNs and ViTs—0
Robustness via Deep Low-Rank Representations—0
Introducing Fuzzy Layers for Deep Learning—0
Introducing the structural bases of typicality effects in deep learning—0
Introduction to Camera Pose Estimation with Deep Learning—0
Introspection for convolutional automatic speech recognition—0
Intrusion Detection: Machine Learning Baseline Calculations for Image Classification—0
Intuitionistic Fuzzy Cognitive Maps for Interpretable Image Classification—0
Invariance-Guided Feature Evolution for Few-Shot Learning—0
Invariance vs Robustness of Neural Networks—0
Invariant Attribute Profiles: A Spatial-Frequency Joint Feature Extractor for Hyperspectral Image Classification—0
Invariant Learning via Diffusion Dreamed Distribution Shifts—0
Invariant Scattering Transform for Medical Imaging—0
Inverse-Free Fast Natural Gradient Descent Method for Deep Learning—0
Inverted Semantic-Index for Image Retrieval—0
Inverting The Generator Of A Generative Adversarial Network—0
Investigating a Baseline Of Self Supervised Learning Towards Reducing Labeling Costs For Image Classification—0
Investigating Bias in Image Classification using Model Explanations—0
Investigating Calibration and Corruption Robustness of Post-hoc Pruned Perception CNNs: An Image Classification Benchmark Study—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