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

Showing 451500 of 10419 papers

TitleStatusHype
How to Learn More? Exploring Kolmogorov-Arnold Networks for Hyperspectral Image ClassificationCode1
Real-Time Hand Gesture Recognition: Integrating Skeleton-Based Data Fusion and Multi-Stream CNNCode1
Demonstrating the Efficacy of Kolmogorov-Arnold Networks in Vision TasksCode1
rKAN: Rational Kolmogorov-Arnold NetworksCode1
Seg-LSTM: Performance of xLSTM for Semantic Segmentation of Remotely Sensed ImagesCode1
LayerMerge: Neural Network Depth Compression through Layer Pruning and MergingCode1
BSRBF-KAN: A combination of B-splines and Radial Basis Functions in Kolmogorov-Arnold NetworksCode1
Fine-grained Classes and How to Find ThemCode1
LieRE: Generalizing Rotary Position EncodingsCode1
DenoiseRep: Denoising Model for Representation LearningCode1
Small Scale Data-Free Knowledge DistillationCode1
fKAN: Fractional Kolmogorov-Arnold Networks with trainable Jacobi basis functionsCode1
Scaling Graph Convolutions for Mobile VisionCode1
Mind's Eye: Image Recognition by EEG via Multimodal Similarity-Keeping Contrastive LearningCode1
UniUSNet: A Promptable Framework for Universal Ultrasound Disease Prediction and Tissue SegmentationCode1
MultiMax: Sparse and Multi-Modal Attention LearningCode1
Improved Generation of Adversarial Examples Against Safety-aligned LLMsCode1
4-bit Shampoo for Memory-Efficient Network TrainingCode1
DMT-JEPA: Discriminative Masked Targets for Joint-Embedding Predictive ArchitectureCode1
Confidence-aware multi-modality learning for eye disease screeningCode1
Segformer++: Efficient Token-Merging Strategies for High-Resolution Semantic SegmentationCode1
Reproducibility Study of CDUL: CLIP-Driven Unsupervised Learning for Multi-Label Image ClassificationCode1
Feature-based Federated Transfer Learning: Communication Efficiency, Robustness and PrivacyCode1
Harnessing the power of longitudinal medical imaging for eye disease prognosis using Transformer-based sequence modelingCode1
Achieving Fairness Through Channel Pruning for Dermatological Disease DiagnosisCode1
Explainable Convolutional Neural Networks for Retinal Fundus Classification and Cutting-Edge Segmentation Models for Retinal Blood Vessels from Fundus ImagesCode1
Differentiable Model Scaling using Differentiable TopkCode1
TAI++: Text as Image for Multi-Label Image Classification by Co-Learning Transferable PromptCode1
Pseudo-Prompt Generating in Pre-trained Vision-Language Models for Multi-Label Medical Image ClassificationCode1
Explanation as a Watermark: Towards Harmless and Multi-bit Model Ownership Verification via Watermarking Feature AttributionCode1
Spectral-Spatial Mamba for Hyperspectral Image ClassificationCode1
Leveraging Cross-Modal Neighbor Representation for Improved CLIP ClassificationCode1
Rethinking model prototyping through the MedMNIST+ dataset collectionCode1
CoProNN: Concept-based Prototypical Nearest Neighbors for Explaining Vision ModelsCode1
Pyramid Hierarchical Transformer for Hyperspectral Image ClassificationCode1
A Comprehensive Survey for Hyperspectral Image Classification: The Evolution from Conventional to Transformers and Mamba ModelsCode1
Next Generation Loss Function for Image ClassificationCode1
InfoMatch: Entropy Neural Estimation for Semi-Supervised Image ClassificationCode1
Fast Fishing: Approximating BAIT for Efficient and Scalable Deep Active Image ClassificationCode1
Variational Stochastic Gradient Descent for Deep Neural NetworksCode1
PairAug: What Can Augmented Image-Text Pairs Do for Radiology?Code1
Label Propagation for Zero-shot Classification with Vision-Language ModelsCode1
Sparse Concept Bottleneck Models: Gumbel Tricks in Contrastive LearningCode1
DeiT-LT Distillation Strikes Back for Vision Transformer Training on Long-Tailed DatasetsCode1
Improving Visual Recognition with Hyperbolical Visual Hierarchy MappingCode1
Can Biases in ImageNet Models Explain Generalization?Code1
Learn "No" to Say "Yes" Better: Improving Vision-Language Models via NegationsCode1
Enhance Image Classification via Inter-Class Image Mixup with Diffusion ModelCode1
Low-Rank Rescaled Vision Transformer Fine-Tuning: A Residual Design ApproachCode1
Targeted Visualization of the Backbone of Encoder LLMsCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CoCa (finetuned)Top 1 Accuracy91Unverified
2Model soups (BASIC-L)Top 1 Accuracy90.98Unverified
3Model soups (ViT-G/14)Top 1 Accuracy90.94Unverified
4DaViT-GTop 1 Accuracy90.4Unverified
5Meta Pseudo Labels (EfficientNet-L2)Top 1 Accuracy90.2Unverified
6DaViT-HTop 1 Accuracy90.2Unverified
7SwinV2-GTop 1 Accuracy90.17Unverified
8MAWS (ViT-6.5B)Top 1 Accuracy90.1Unverified
9Florence-CoSwin-HTop 1 Accuracy90.05Unverified
10RevCol-HTop 1 Accuracy90Unverified