A multilevel perceptual CRF model using Swin Transformer, HPF fusion, HA adapters, and dynamic scaling attention achieves state-of-the-art monocular depth estimation on NYU Depth v2, KITTI, and MatterPort3D with reduced error and fast inference.
Textpecker: Rewarding structural anomaly quantifica- tion for enhancing visual text rendering
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
fields
cs.CV 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
RDCNet reports state-of-the-art accuracy on CIFAR-10, CIFAR-100, SVHN, Imagenette, and Imagewoof by combining random dilated convolutions with multi-branch and attention modules.
citing papers explorer
-
Hierarchical Awareness Adapters with Hybrid Pyramid Feature Fusion for Dense Depth Prediction
A multilevel perceptual CRF model using Swin Transformer, HPF fusion, HA adapters, and dynamic scaling attention achieves state-of-the-art monocular depth estimation on NYU Depth v2, KITTI, and MatterPort3D with reduced error and fast inference.
-
Image Classification via Random Dilated Convolution with Multi-Branch Feature Extraction and Context Excitation
RDCNet reports state-of-the-art accuracy on CIFAR-10, CIFAR-100, SVHN, Imagenette, and Imagewoof by combining random dilated convolutions with multi-branch and attention modules.