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 5801–5850 of 10420 papers

TitleStatusHype
Detecting Adversarial Examples in Convolutional Neural Networks—0
NIST: An Image Classification Network to Image Semantic Retrieval—0
Learning The Structure of Deep Convolutional Networks—0
Learning Through Guidance: Knowledge Distillation for Endoscopic Image Classification—0
Learning to Adapt Category Consistent Meta-Feature of CLIP for Few-Shot Classification—0
Detecting Adversarial Perturbations Through Spatial Behavior in Activation Spaces—0
Learning to aggregate feature representations—0
Learning to be Global Optimizer—0
COVIDLite: A depth-wise separable deep neural network with white balance and CLAHE for detection of COVID-19—0
MambaMixer: Efficient Selective State Space Models with Dual Token and Channel Selection—0
Manifold Graph with Learned Prototypes for Semi-Supervised Image Classification—0
Learning To Collaborate in Decentralized Learning of Personalized Models—0
Mapping Biological Neuron Dynamics into an Interpretable Two-layer Artificial Neural Network—0
Masked Reconstruction Contrastive Learning with Information Bottleneck Principle—0
Mayfly optimization with deep learning enabled retinal fundus image classification model—0
Learning to Complement with Multiple Humans—0
Learning to Detect Blue-white Structures in Dermoscopy Images with Weak Supervision—0
Learning to Detect Malicious Clients for Robust Federated Learning—0
Learning to Detect Semantic Boundaries with Image-level Class Labels—0
Beyond the Attention: Distinguish the Discriminative and Confusable Features For Fine-grained Image Classification—0
Learning to Find Correlated Features by Maximizing Information Flow in Convolutional Neural Networks—0
Meta Approach to Data Augmentation Optimization—0
Accelerated PDEs for Construction and Theoretical Analysis of an SGD Extension—0
Learning to Generate Images with Perceptual Similarity Metrics—0
Learning to generate imaginary tasks for improving generalization in meta-learning—0
Detecting Novelties with Empty Classes—0
An Analysis of Pre-Training on Object Detection—0
Learning-to-learn enables rapid learning with phase-change memory-based in-memory computing—0
Detecting Overfitting via Adversarial Examples—0
Learning to Learn: How to Continuously Teach Humans and Machines—0
Learning to Learn Image Classifiers with Visual Analogy—0
Attending Category Disentangled Global Context for Image Classification—0
Learning to Learn Semantic Factors in Heterogeneous Image Classification—0
Making Vision Transformers Truly Shift-Equivariant—0
GROOD: Gradient-Aware Out-of-Distribution Detection—0
Covid-19: Automatic detection from X-Ray images utilizing Transfer Learning with Convolutional Neural Networks—0
A Hybrid Feature Fusion Deep Learning Framework for Leukemia Cancer Detection in Microscopic Blood Sample Using Gated Recurrent Unit and Uncertainty Quantification—0
Detecting Visually Relevant Sentences for Fine-Grained Classification—0
How Out-of-Distribution Detection Learning Theory Enhances Transformer: Learnability and Reliability—0
Coverage Testing of Deep Learning Models using Dataset Characterization—0
Mako: Semi-supervised continual learning with minimal labeled data via data programming—0
flexgrid2vec: Learning Efficient Visual Representations Vectors—0
Detection of Children Abuse by Voice and Audio Classification by Short-Time Fourier Transform Machine Learning implemented on Nvidia Edge GPU device—0
Learning to Sample: an Active Learning Framework—0
Learning to Schedule Learning rate with Graph Neural Networks—0
Learning to see across Domains and Modalities—0
A Hybrid Differential Evolution Approach to Designing Deep Convolutional Neural Networks for Image Classification—0
Learning and Sharing: A Multitask Genetic Programming Approach to Image Feature Learning—0
Learning to Specialize with Knowledge Distillation for Visual Question Answering—0
Covariance-corrected Whitening Alleviates Network Degeneration on Imbalanced Classification—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