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 6951–6975 of 10420 papers

TitleStatusHype
Knowledge Distillation with Feature Maps for Image Classification—0
Bayesian Federated Neural Matching that Completes Full Information—0
An Access Control Method with Secret Key for Semantic Segmentation Models—0
Adaptive Periodic Averaging: A Practical Approach to Reducing Communication in Distributed Learning—0
Achieving Explainability in a Visual Hard Attention Model through Content Prediction—0
Offloading Algorithms for Maximizing Inference Accuracy on Edge Device Under a Time Constraint—0
KOALA++: Efficient Kalman-Based Optimization of Neural Networks with Gradient-Covariance Products—0
Knowledge distillation using unlabeled mismatched images—0
Knowledge Distillation Methods for Efficient Unsupervised Adaptation Across Multiple Domains—0
OmniVec2 - A Novel Transformer based Network for Large Scale Multimodal and Multitask Learning—0
Knowledge Distillation in Vision Transformers: A Critical Review—0
OmniVL:One Foundation Model for Image-Language and Video-Language Tasks—0
Deep Predictive Coding Network for Object Recognition—0
Bayesian Exploration of Pre-trained Models for Low-shot Image Classification—0
Knowledge Distillation in Generations: More Tolerant Teachers Educate Better Students—0
Knowledge Distillation for Object Detection via Rank Mimicking and Prediction-guided Feature Imitation—0
Deep Poisoning: Towards Robust Image Data Sharing against Visual Disclosure—0
Bayesian Convolutional Neural Networks for Limited Data Hyperspectral Remote Sensing Image Classification—0
AMUN: Adversarial Machine UNlearning—0
Knowledge Distillation for Incremental Learning in Semantic Segmentation—0
Knowledge Concentration: Learning 100K Object Classifiers in a Single CNN—0
Knowledge-Aware Prompt Tuning for Generalizable Vision-Language Models—0
Knowledge accumulating: The general pattern of learning—0
Deep Octonion Networks—0
Adaptive Noise-Tolerant Network for Image Segmentation—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
10Meta Pseudo Labels (EfficientNet-B6-Wide)Top 1 Accuracy90—Unverified