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 9851–9875 of 10420 papers

TitleStatusHype
In Automation We Trust: Investigating the Role of Uncertainty in Active Learning Systems—0
InceptionCapsule: Inception-Resnet and CapsuleNet with self-attention for medical image Classification—0
Incomplete Dot Products for Dynamic Computation Scaling in Neural Network Inference—0
In-context learning enables multimodal large language models to classify cancer pathology images—0
In-Context Learning for Label-Efficient Cancer Image Classification in Oncology—0
Incoporating Weighted Board Learning System for Accurate Occupational Pneumoconiosis Staging—0
Incorporating Semantic Attention in Video Description Generation—0
Increasing Model Generalizability for Unsupervised Domain Adaptation—0
Increasing Shape Bias in ImageNet-Trained Networks Using Transfer Learning and Domain-Adversarial Methods—0
Increasing the Inference and Learning Speed of Tsetlin Machines with Clause Indexing—0
Increasing the Robustness of Semantic Segmentation Models with Painting-by-Numbers—0
Increasing Trustworthiness of Deep Neural Networks via Accuracy Monitoring—0
Incremental Few-Shot Learning via Implanting and Compressing—0
Incremental Learning in Deep Convolutional Neural Networks Using Partial Network Sharing—0
Incremental Learning In Online Scenario—0
Incremental Learning of NCM Forests for Large-Scale Image Classification—0
Incremental Learning Through Deep Adaptation—0
Incremental Learning with Differentiable Architecture and Forgetting Search—0
Incremental multi-domain learning with network latent tensor factorization—0
Incremental Online Learning Algorithms Comparison for Gesture and Visual Smart Sensors—0
Incremental Open-set Domain Adaptation—0
In-depth Question classification using Convolutional Neural Networks—0
In-Domain Self-Supervised Learning Improves Remote Sensing Image Scene Classification—0
Indoor image representation by high-level semantic features—0
Inducing and Exploiting Activation Sparsity for Fast Inference on Deep Neural Networks—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
5DaViT-HTop 1 Accuracy90.2—Unverified
6Meta Pseudo Labels (EfficientNet-L2)Top 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