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

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Showing 3201–3250 of 10419 papers

TitleStatusHype
Domain Aligned CLIP for Few-shot Classification—0
Domain-decomposed image classification algorithms using linear discriminant analysis and convolutional neural networks—0
Domain Expansion and Boundary Growth for Open-Set Single-Source Domain Generalization—0
Black Box to White Box: Discover Model Characteristics Based on Strategic Probing—0
Building Human-like Communicative Intelligence: A Grounded Perspective—0
An efficient and flexible inference system for serving heterogeneous ensembles of deep neural networks—0
A New Distance Measure for Non-Identical Data with Application to Image Classification—0
ADINet: Attribute Driven Incremental Network for Retinal Image Classification—0
DIME-FM : DIstilling Multimodal and Efficient Foundation Models—0
Buildings Detection in VHR SAR Images Using Fully Convolution Neural Networks—0
Domain transfer through deep activation matching—0
Domain Wall Magnetic Tunnel Junction Reliable Integrate and Fire Neuron—0
Do More Dropouts in Pool5 Feature Maps for Better Object Detection—0
Bundle Optimization for Multi-aspect Embedding—0
Addressing Discrepancies in Semantic and Visual Alignment in Neural Networks—0
DIME-FM: DIstilling Multimodal and Efficient Foundation Models—0
A Compact Representation of Histopathology Images using Digital Stain Separation & Frequency-Based Encoded Local Projections—0
Don’t Wait, Just Weight: Improving Unsupervised Representations by Learning Goal-Driven Instance Weights—0
Exploring Low-Resource Medical Image Classification with Weakly Supervised Prompt Learning—0
Dopamine Transporter SPECT Image Classification for Neurodegenerative Parkinsonism via Diffusion Maps and Machine Learning Classifiers—0
An Effective Label Noise Model for DNN Text Classification—0
Black Box Explanation by Learning Image Exemplars in the Latent Feature Space—0
Exploring Explainability in Video Action Recognition—0
Double-Stage Feature-Level Clustering-Based Mixture of Experts Framework—0
Double Transfer Learning for Breast Cancer Histopathologic Image Classification—0
Doubly Convolutional Neural Networks—0
Dilated Deep Residual Network for Image Denoising—0
Doubly Sparse: Sparse Mixture of Sparse Experts for Efficient Softmax Inference—0
C2AE: Class Conditioned Auto-Encoder for Open-set Recognition—0
Black-box adversarial attacks using Evolution Strategies—0
Exploring Explainability Methods for Graph Neural Networks—0
Black-box Adversarial Attacks on Monocular Depth Estimation Using Evolutionary Multi-objective Optimization—0
An Effective Gram Matrix Characterizes Generalization in Deep Networks—0
Exploring Feature Reuse in DenseNet Architectures—0
Exploring Modality Guidance to Enhance VFM-based Feature Fusion for UDA in 3D Semantic Segmentation—0
Exploring the significance of using perceptually relevant image decolorization method for scene classification—0
Extracurricular Learning: Knowledge Transfer Beyond Empirical Distribution—0
Diffusion models applied to skin and oral cancer classification—0
An Effective Fusion Method to Enhance the Robustness of CNN—0
Exploring Cross-Domain Pretrained Model for Hyperspectral Image Classification—0
A New Linear Scaling Rule for Private Adaptive Hyperparameter Optimization—0
DPSNN: A Differentially Private Spiking Neural Network with Temporal Enhanced Pooling—0
Diff-SySC: An Approach Using Diffusion Models for Semi-Supervised Image Classification—0
DQA: An Efficient Method for Deep Quantization of Deep Neural Network Activations—0
DiffSpectralNet : Unveiling the Potential of Diffusion Models for Hyperspectral Image Classification—0
Drawing Multiple Augmentation Samples Per Image During Training Efficiently Decreases Test Error—0
Drift to Remember—0
DRO-Augment Framework: Robustness by Synergizing Wasserstein Distributionally Robust Optimization and Data Augmentation—0
An Extendable, Efficient and Effective Transformer-based Object Detector—0
Bit-aware Randomized Response for Local Differential Privacy in Federated 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
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