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 5201–5250 of 10420 papers

TitleStatusHype
Addressing out-of-distribution label noise in webly-labelled data—0
Defensive Tensorization—0
VAC-CNN: A Visual Analytics System for Comparative Studies of Deep Convolutional Neural Networks—0
MUSE: Feature Self-Distillation with Mutual Information and Self-Information—0
ZerO Initialization: Initializing Neural Networks with only Zeros and OnesCode1
Instance-Conditional Knowledge Distillation for Object DetectionCode1
Progressively Select and Reject Pseudo-labelled Samples for Open-Set Domain Adaptation—0
Some like it tough: Improving model generalization via progressively increasing the training difficultyCode0
Exploring Gradient Flow Based Saliency for DNN Model CompressionCode0
Generalized Resubstitution for Classification Error Estimation—0
MisMatch: Calibrated Segmentation via Consistency on Differential Morphological Feature Perturbations with Limited LabelsCode1
Attend and Guide (AG-Net): A Keypoints-driven Attention-based Deep Network for Image RecognitionCode0
Game of Gradients: Mitigating Irrelevant Clients in Federated LearningCode0
A Simple Baseline for Low-Budget Active LearningCode1
Federated Unlearning via Class-Discriminative PruningCode0
Sinkformers: Transformers with Doubly Stochastic AttentionCode1
Signature-Graph Networks—0
PropMix: Hard Sample Filtering and Proportional MixUp for Learning with Noisy LabelsCode0
Recurrence along Depth: Deep Convolutional Neural Networks with Recurrent Layer AggregationCode1
Logical Activation Functions: Logit-space equivalents of Probabilistic Boolean Operators—0
GCCN: Global Context Convolutional Network—0
EnGraf-Net: Multiple Granularity Branch Network with Fine-Coarse Graft Grained for Classification TaskCode0
FedGEMS: Federated Learning of Larger Server Models via Selective Knowledge Fusion—0
Grafting Transformer on Automatically Designed Convolutional Neural Network for Hyperspectral Image ClassificationCode1
Improving the Deployment of Recycling Classification through Efficient Hyper-Parameter Analysis—0
Does Data Repair Lead to Fair Models? Curating Contextually Fair Data To Reduce Model BiasCode0
Repaint: Improving the Generalization of Down-Stream Visual Tasks by Generating Multiple Instances of Training ExamplesCode0
DARTS for Inverse Problems: a Study on Stability—0
Learning Partial Equivariances from DataCode1
An Adaptive Sampling and Edge Detection Approach for Encoding Static Images for Spiking Neural Networks—0
Improving Tail-Class Representation with Centroid Contrastive Learning—0
When in Doubt, Summon the Titans: Efficient Inference with Large Models—0
Multi-concept adversarial attacks—0
EmbRace: Accelerating Sparse Communication for Distributed Training of NLP Neural Networks—0
Deep CNNs for Peripheral Blood Cell Classification—0
HRFormer: High-Resolution Transformer for Dense PredictionCode1
Training Deep Neural Networks with Adaptive Momentum Inspired by the Quadratic OptimizationCode1
Alleviating Noisy-label Effects in Image Classification via Probability Transition Matrix—0
Online Continual Learning Via Candidates Voting—0
Contrastive Learning of Visual-Semantic Embeddings—0
Network Augmentation for Tiny Deep Learning—0
Neural Network Pruning Through Constrained Reinforcement Learning—0
TESDA: Transform Enabled Statistical Detection of Attacks in Deep Neural NetworksCode0
Pro-KD: Progressive Distillation by Following the Footsteps of the Teacher—0
FedSLD: Federated Learning with Shared Label Distribution for Medical Image Classification—0
Adversarial Attacks on ML Defense Models CompetitionCode1
Trade-offs of Local SGD at Scale: An Empirical Study—0
Automated Quality Control of Vacuum Insulated Glazing by Convolutional Neural Network Image Classification—0
FlexConv: Continuous Kernel Convolutions with Differentiable Kernel SizesCode1
Reliable Deep Learning Plant Leaf Disease Classification Based on Light-Chroma Separated BranchesCode1
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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