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 451475 of 10419 papers

TitleStatusHype
How to Learn More? Exploring Kolmogorov-Arnold Networks for Hyperspectral Image ClassificationCode1
Demonstrating the Efficacy of Kolmogorov-Arnold Networks in Vision TasksCode1
Real-Time Hand Gesture Recognition: Integrating Skeleton-Based Data Fusion and Multi-Stream CNNCode1
Seg-LSTM: Performance of xLSTM for Semantic Segmentation of Remotely Sensed ImagesCode1
rKAN: Rational Kolmogorov-Arnold NetworksCode1
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
MultiMax: Sparse and Multi-Modal Attention LearningCode1
UniUSNet: A Promptable Framework for Universal Ultrasound Disease Prediction and Tissue SegmentationCode1
Confidence-aware multi-modality learning for eye disease screeningCode1
4-bit Shampoo for Memory-Efficient Network TrainingCode1
DMT-JEPA: Discriminative Masked Targets for Joint-Embedding Predictive ArchitectureCode1
Improved Generation of Adversarial Examples Against Safety-aligned LLMsCode1
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
Achieving Fairness Through Channel Pruning for Dermatological Disease DiagnosisCode1
Harnessing the power of longitudinal medical imaging for eye disease prognosis using Transformer-based sequence modelingCode1
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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