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 4901–4950 of 10420 papers

TitleStatusHype
Exploring Deep Learning Methods for Classification of SAR Images: Towards NextGen Convolutions via Transformers—0
Collaborative Image Understanding—0
A Random Ensemble of Encrypted Vision Transformers for Adversarially Robust Defense—0
A Continual Learning Framework for Adaptive Defect Classification and Inspection—0
AAVAE: Augmentation-Augmented Variational Autoencoders—0
WaveMamba: Spatial-Spectral Wavelet Mamba for Hyperspectral Image Classification—0
Advancements in Image Classification using Convolutional Neural Network—0
Exploring Data Augmentations on Self-/Semi-/Fully- Supervised Pre-trained Models—0
Exploring Cross-Domain Pretrained Model for Hyperspectral Image Classification—0
Exploring Category-correlated Feature for Few-shot Image Classification—0
Exploring Camera Encoder Designs for Autonomous Driving Perception—0
Collaboration of Experts: Achieving 80% Top-1 Accuracy on ImageNet with 100M FLOPs—0
Exploring Adversarial Attacks against Latent Diffusion Model from the Perspective of Adversarial Transferability—0
Explore the Power of Dropout on Few-shot Learning—0
Explore the Knowledge contained in Network Weights to Obtain Sparse Neural Networks—0
CoLa-DCE -- Concept-guided Latent Diffusion Counterfactual Explanations—0
Adversarial Robustness on Image Classification with k-means—0
Explore the Effect of Data Selection on Poison Efficiency in Backdoor Attacks—0
Exploration of Noise Strategies in Semi-supervised Named Entity Classification—0
CognitiveNet: Enriching Foundation Models with Emotions and Awareness—0
Exploration of Multi-Scale Image Fusion Systems in Intelligent Medical Image Analysis—0
Detached Error Feedback for Distributed SGD with Random Sparsification—0
CoDiM: Learning with Noisy Labels via Contrastive Semi-Supervised Learning—0
A Push-Pull Layer Improves Robustness of Convolutional Neural Networks—0
Exploiting Web Images for Dataset Construction: A Domain Robust Approach—0
Exploiting the relationship between visual and textual features in social networks for image classification with zero-shot deep learning—0
Exploiting the Full Capacity of Deep Neural Networks while Avoiding Overfitting by Targeted Sparsity Regularization—0
Exploiting Patch Sizes and Resolutions for Multi-Scale Deep Learning in Mammogram Image Classification—0
A proof that artificial neural networks overcome the curse of dimensionality in the numerical approximation of Black-Scholes partial differential equations—0
Adversarial Robustness in Deep Learning: Attacks on Fragile Neurons—0
Exploiting Nontrivial Connectivity for Automatic Speech Recognition—0
Exploiting Local Features from Deep Networks for Image Retrieval—0
Exploiting LMM-based knowledge for image classification tasks—0
Code-Bridged Classifier (CBC): A Low or Negative Overhead Defense for Making a CNN Classifier Robust Against Adversarial Attacks—0
Exploiting Kernel Compression on BNNs—0
Exploiting Image-trained CNN Architectures for Unconstrained Video Classification—0
Exploiting Contextual Uncertainty of Visual Data for Efficient Training of Deep Models—0
COBRA: COmBinatorial Retrieval Augmentation for Few-Shot Adaptation—0
A Progressive Framework of Vision-language Knowledge Distillation and Alignment for Multilingual Scene—0
Exploiting Category Names for Few-Shot Classification with Vision-Language Models—0
Exploiting Activation based Gradient Output Sparsity to Accelerate Backpropagation in CNNs—0
Explicitly Modeled Attention Maps for Image Classification—0
A probabilistic patch based image representation using Conditional Random Field model for image classification—0
Explicit Domain Adaptation with Loosely Coupled Samples—0
Explicit Connection Distillation—0
Coarse to Fine: Multi-label Image Classification with Global/Local Attention—0
Explanatory Masks for Neural Network Interpretability—0
CoAPT: Context Attribute words for Prompt Tuning—0
A Probabilistic Model for Joint Learning of Word Embeddings from Texts and Images—0
Adversarial Robustness Assessment of NeuroEvolution Approaches—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