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 951–975 of 10419 papers

TitleStatusHype
Multi-Cohort Framework with Cohort-Aware Attention and Adversarial Mutual-Information Minimization for Whole Slide Image Classification—0
Anti-ESIA: Analyzing and Mitigating Impacts of Electromagnetic Signal Injection Attacks—0
Are Deep Learning Models Robust to Partial Object Occlusion in Visual Recognition Tasks?—0
Kolmogorov-Arnold TransformerCode4
Frequency-Guided Masking for Enhanced Vision Self-Supervised LearningCode0
InfoDisent: Explainability of Image Classification Models by Information Disentanglement—0
Enhancing Image Classification in Small and Unbalanced Datasets through Synthetic Data Augmentation—0
Finetuning CLIP to Reason about Pairwise DifferencesCode1
SparX: A Sparse Cross-Layer Connection Mechanism for Hierarchical Vision Mamba and Transformer NetworksCode1
Leveraging Foundation Models for Efficient Federated Learning in Resource-restricted Edge Networks—0
One missing piece in Vision and Language: A Survey on Comics UnderstandingCode2
Real-world Adversarial Defense against Patch Attacks based on Diffusion ModelCode1
Byzantine-Robust and Communication-Efficient Distributed Learning via Compressed Momentum Filtering—0
Anytime Continual Learning for Open Vocabulary ClassificationCode1
Pushing Joint Image Denoising and Classification to the Edge—0
Microscopic-Mamba: Revealing the Secrets of Microscopic Images with Just 4M ParametersCode0
Efficient Privacy-Preserving KAN Inference Using Homomorphic Encryption—0
DFDG: Data-Free Dual-Generator Adversarial Distillation for One-Shot Federated Learning—0
Enhancing Few-Shot Image Classification through Learnable Multi-Scale Embedding and Attention MechanismsCode1
Classifying Images with CoLaNET Spiking Neural Network -- the MNIST Example—0
A Contrastive Symmetric Forward-Forward Algorithm (SFFA) for Continual Learning Tasks—0
Minimizing Embedding Distortion for Robust Out-of-Distribution Performance—0
Optimizing Neural Network Performance and Interpretability with Diophantine Equation Encoding—0
Token Turing Machines are Efficient Vision ModelsCode0
Privacy-Preserving Federated Learning with Consistency via Knowledge Distillation Using Conditional Generator—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