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 7651–7700 of 10420 papers

TitleStatusHype
AME: Attention and Memory Enhancement in Hyper-Parameter Optimization—0
Adaptive Early Exiting for Collaborative Inference over Noisy Wireless Channels—0
Pseudo Labels for Single Positive Multi-Label Learning—0
Accurate and Resource-Efficient Lipreading with Efficientnetv2 and Transformers—0
MCU: Improving Machine Unlearning through Mode Connectivity—0
Pseudo Rehearsal using non photo-realistic images—0
Indoor image representation by high-level semantic features—0
In-Domain Self-Supervised Learning Improves Remote Sensing Image Scene Classification—0
PSLT: A Light-weight Vision Transformer with Ladder Self-Attention and Progressive Shift—0
Deep Hierarchical Machine: a Flexible Divide-and-Conquer Architecture—0
PSO-PS: Parameter Synchronization with Particle Swarm Optimization for Distributed Training of Deep Neural Networks—0
Deep Hashing: A Joint Approach for Image Signature Learning—0
In-depth Question classification using Convolutional Neural Networks—0
PUDD: Towards Robust Multi-modal Prototype-based Deepfake Detection—0
Pulmonary embolism identification in computerized tomography pulmonary angiography scans with deep learning technologies in COVID-19 patients—0
A Web Page Classifier Library Based on Random Image Content Analysis Using Deep Learning—0
Incremental Open-set Domain Adaptation—0
Incremental Online Learning Algorithms Comparison for Gesture and Visual Smart Sensors—0
Incremental multi-domain learning with network latent tensor factorization—0
Pushing Joint Image Denoising and Classification to the Edge—0
Incremental Learning with Differentiable Architecture and Forgetting Search—0
Pushing the Limits of Narrow Precision Inferencing at Cloud Scale with Microsoft Floating Point—0
Pushing the Limits of Radiology with Joint Modeling of Visual and Textual Information—0
Incremental Learning Through Deep Adaptation—0
Improving Shape Awareness and Interpretability in Deep Networks Using Geometric Moments—0
Incremental Learning of NCM Forests for Large-Scale Image Classification—0
Putting GPT-4o to the Sword: A Comprehensive Evaluation of Language, Vision, Speech, and Multimodal Proficiency—0
Incremental Learning In Online Scenario—0
Deep Generative Modeling for Protein Design—0
A Weakly Supervised Fine Label Classifier Enhanced by Coarse Supervision—0
AMD: Automatic Multi-step Distillation of Large-scale Vision Models—0
Incremental Learning in Deep Convolutional Neural Networks Using Partial Network Sharing—0
Incremental Few-Shot Learning via Implanting and Compressing—0
Increasing Trustworthiness of Deep Neural Networks via Accuracy Monitoring—0
Pyramidal RoR for Image Classification—0
PyramidCLIP: Hierarchical Feature Alignment for Vision-language Model Pretraining—0
Increasing the Robustness of Semantic Segmentation Models with Painting-by-Numbers—0
Deep Gaussian Processes with Convolutional Kernels—0
Increasing the Inference and Learning Speed of Tsetlin Machines with Clause Indexing—0
Increasing Shape Bias in ImageNet-Trained Networks Using Transfer Learning and Domain-Adversarial Methods—0
Increasing Model Generalizability for Unsupervised Domain Adaptation—0
Avoiding Overfitting: A Survey on Regularization Methods for Convolutional Neural Networks—0
Incorporating Semantic Attention in Video Description Generation—0
Deep Fisher Networks for Large-Scale Image Classification—0
Deep FisherNet for Object Classification—0
Q-GADMM: Quantized Group ADMM for Communication Efficient Decentralized Machine Learning—0
Incoporating Weighted Board Learning System for Accurate Occupational Pneumoconiosis Staging—0
In-Context Learning for Label-Efficient Cancer Image Classification in Oncology—0
Deep Features for training Support Vector Machine—0
In-context learning enables multimodal large language models to classify cancer pathology images—0
Show:102550
← PrevPage 154 of 209Next →
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