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 6476–6500 of 10420 papers

TitleStatusHype
Domain-Invariant Disentangled Network for Generalizable Object Detection—0
Distilling Global and Local Logits With Densely Connected RelationsCode0
Student Customized Knowledge Distillation: Bridging the Gap Between Student and Teacher—0
Kernel Methods in Hyperbolic Spaces—0
Synthesized Feature Based Few-Shot Class-Incremental Learning on a Mixture of Subspaces—0
P-Swish: Activation Function with Learnable Parameters Based on Swish Activation Function in Deep Learning—0
Energy-constrained Self-training for Unsupervised Domain Adaptation—0
Iranis: A Large-scale Dataset of Farsi License Plate CharactersCode1
Active Learning Under Malicious Mislabeling and Poisoning Attacks—0
Generative Max-Mahalanobis Classifiers for Image Classification, Generation and MoreCode0
AFINets: Attentive Feature Integration Networks for Image Classification—0
Exploring Target Driven Image Classification—0
Protecting DNNs from Theft using an Ensemble of Diverse Models—0
USING OBJECT-FOCUSED IMAGES AS AN IMAGE AUGMENTATION TECHNIQUE TO IMPROVE THE ACCURACY OF IMAGE-CLASSIFICATION MODELS WHEN VERY LIMITED DATA SETS ARE AVAILABLE—0
Detection Booster Training: A detection booster training method for improving the accuracy of classifiers.—0
Learning the Connections in Direct Feedback Alignment—0
Improve Object Detection with Feature-based Knowledge Distillation: Towards Accurate and Efficient DetectorsCode1
General Adversarial Defense via Pixel Level and Feature Level Distribution Alignment—0
Policy-Driven Attack: Learning to Query for Hard-label Black-box Adversarial Examples—0
Toward Understanding Supervised Representation Learning with RKHS and GAN—0
Explicit Connection Distillation—0
CNN Based Analysis of the Luria’s Alternating Series Test for Parkinson’s Disease Diagnostics—0
Unsupervised Domain Adaptation via Minimized Joint Error—0
Category Disentangled Context: Turning Category-irrelevant Features Into Treasures—0
Towards Practical Second Order Optimization for Deep Learning—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