Some lightweight SR methods solve this issue by directly designing shallower architectures, but it will adversely affect the representation capability of convolutional neural networks. To address …

Jan 24, 2022 · To address this issue, we propose the dual feature aggregation strategy for image SR. It enhances feature utilization via feature reuse, which largely improves the representation …

To address this issue, we propose the dual feature aggregation strategy for image SR. It enhances feature utilization via feature reuse, which largely improves the representation ability …

Jan 24, 2022 · To address this issue, we propose the dual feature aggregation strategy for image SR. It enhances feature utilization via feature reuse, which largely improves the representation …

PyTorch implementation of the hourglass architecture proposed in "Single-Image Depth Perception in the Wild" by Chen, et al. in NIPS 2016. Adapted for surface normal estimation …

Jan 24, 2022 · This work proposes the dual feature aggregation strategy for image SR, which enhances feature utilization via feature reuse, which largely improves the representation ability …

The proposed DFAN-IQA method exploits the pre-trained ViT and ResNet50 to extract attention features and semantic features from a distorted image, respectively. In addition, we have …

The DFAN adopts an adaptive aggregation model (AAM), which combines the orthogonal spectral attention module (OSAM) with the background anomaly category statistics module. This allows …

Do you see things in dreams? Yep, I dream fine, in pictures and everything. Interestingly, I do start to see things as I am transitioning into a dream state. I can sometimes pull myself out as …

We propose an effective Dual-Feature Aggregation Network (DFAN) for NR-IQA by using the Convolutional Neural Networks (CNN) called ResNet50 and Vision Transformer (ViT).