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 4251–4300 of 10420 papers

TitleStatusHype
Remote Sensing Image Classification using Transfer Learning and Attention Based Deep Neural Network—0
When Does Re-initialization Work?—0
Classification Utility, Fairness, and Compactness via Tunable Information Bottleneck and Rényi MeasuresCode0
Revisiting lp-constrained Softmax Loss: A Comprehensive StudyCode0
Using Sum-Product Networks to Assess Uncertainty in Deep Active Learning—0
Contextual Squeeze-and-Excitation for Efficient Few-Shot Image ClassificationCode1
Global Context Vision TransformersCode2
Out-of-distribution Detection by Cross-class Vicinity Distribution of In-distribution DataCode0
EATFormer: Improving Vision Transformer Inspired by Evolutionary AlgorithmCode1
0/1 Deep Neural Networks via Block Coordinate Descent—0
Terrain Classification using Transfer Learning on Hyperspectral Images: A Comparative study—0
Transform-Invariant Convolutional Neural Networks for Image Classification and Search—0
Design of Supervision-Scalable Learning Systems: Methodology and Performance Benchmarking—0
A Comparative Study of Confidence Calibration in Deep Learning: From Computer Vision to Medical Imaging—0
DGMIL: Distribution Guided Multiple Instance Learning for Whole Slide Image ClassificationCode1
Minimum Noticeable Difference based Adversarial Privacy Preserving Image Generation—0
Detecting Adversarial Examples in Batches -- a geometrical approachCode0
The Importance of Background Information for Out of Distribution Generalization—0
Neural Architecture Adaptation for Object Detection by Searching Channel Dimensions and Mapping Pre-trained Parameters—0
Active Data Discovery: Mining Unknown Data using Submodular Information Measures—0
FiT: Parameter Efficient Few-shot Transfer Learning for Personalized and Federated Image ClassificationCode1
Using adversarial images to improve outcomes of federated learning for non-IID data—0
PRANC: Pseudo RAndom Networks for Compacting deep modelsCode1
Channel Importance Matters in Few-Shot Image ClassificationCode1
Simple and Efficient Architectures for Semantic SegmentationCode1
Boosting the Adversarial Transferability of Surrogate Models with Dark KnowledgeCode1
Open-Set Recognition with Gradient-Based Representations—0
SP-ViT: Learning 2D Spatial Priors for Vision TransformersCode1
Self-Supervised Implicit Attention: Guided Attention by The Model Itself—0
Efficient Adaptive Ensembling for Image Classification—0
Differentiable Top-k Classification LearningCode1
Lessons learned from the NeurIPS 2021 MetaDL challenge: Backbone fine-tuning without episodic meta-learning dominates for few-shot learning image classification—0
Masked Frequency Modeling for Self-Supervised Visual Pre-TrainingCode1
Masked Siamese ConvNets—0
Morphence-2.0: Evasion-Resilient Moving Target Defense Powered by Out-of-Distribution DetectionCode1
Recent Advances in Scene Image Representation and Classification—0
Self-Supervised Pretraining for Differentially Private LearningCode0
Evaluating histopathology transfer learning with ChampKitCode1
Peripheral Vision TransformerCode1
A Survey of Automated Data Augmentation Algorithms for Deep Learning-based Image Classification Tasks—0
Specifying and Testing k-Safety Properties for Machine-Learning ModelsCode0
Efficient Human-in-the-loop System for Guiding DNNs AttentionCode0
Learning Task-Independent Game State Representations from Unlabeled Images—0
2nd Place Solution for ICCV 2021 VIPriors Image Classification Challenge: An Attract-and-Repulse Learning Approach—0
Better Teacher Better Student: Dynamic Prior Knowledge for Knowledge DistillationCode0
Learning-Based Data Storage [Vision] (Technical Report)—0
Training Neural Networks using SAT solvers—0
Learning the Space of Deep ModelsCode0
Saccade Mechanisms for Image Classification, Object Detection and Tracking—0
Masked Autoencoders are Robust Data AugmentorsCode1
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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