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 8626–8650 of 10420 papers

TitleStatusHype
Disrupting Model Merging: A Parameter-Level Defense Without Sacrificing Accuracy—0
Disrupting Model Training with Adversarial Shortcuts—0
Dissonance Between Human and Machine Understanding—0
Distance-based Composable Representations with Neural Networks—0
Distangling Biological Noise in Cellular Images with a focus on Explainability—0
Distant Domain Transfer Learning for Medical Imaging—0
DistilDoc: Knowledge Distillation for Visually-Rich Document Applications—0
StableMamba: Distillation-free Scaling of Large SSMs for Images and Videos—0
Distillation from heterogeneous unlabeled collections—0
Distilling High Diagnostic Value Patches for Whole Slide Image Classification Using Attention Mechanism—0
Distilling Knowledge into Quantum Vision Transformers for Biomedical Image Classification—0
Distilling Self-Supervised Vision Transformers for Weakly-Supervised Few-Shot Classification & Segmentation—0
Distilling Spikes: Knowledge Distillation in Spiking Neural Networks—0
Distortion Robust Image Classification using Deep Convolutional Neural Network with Discrete Cosine Transform—0
Distributed Deep Learning Using Volunteer Computing-Like Paradigm—0
Distributed Federated Learning-Based Deep Learning Model for Privacy MRI Brain Tumor Detection—0
GTAdam: Gradient Tracking with Adaptive Momentum for Distributed Online Optimization—0
Distributed stochastic optimization for deep learning (thesis)—0
Distributed Training of Deep Neural Networks with Theoretical Analysis: Under SSP Setting—0
Distributed Transfer Learning with 4th Gen Intel Xeon Processors—0
Distribution Adaptive INT8 Quantization for Training CNNs—0
Task-Robust Model-Agnostic Meta-Learning—0
Distributional loss for convolutional neural network regression and application to GNSS multi-path estimation—0
Robust Multi-instance Learning with Stable Instances—0
Distributionally Robust Optimization and Invariant Representation Learning for Addressing Subgroup Underrepresentation: Mechanisms and Limitations—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