10 citations · 26 across the 8 of their papers we have counts for
8 papers
LSAS: Lightweight Sub-attention Strategy for Alleviating Attention Bias Problem
Shanshan Zhong, Wushao Wen, Jinghui Qin +2
In computer vision, the performance of deep neural networks (DNNs) is highly related to the feature extraction ability, i.e., the ability to recognize and focus on key pixel region…
SUR-adapter: Enhancing Text-to-Image Pre-trained Diffusion Models with Large Language Models
Shanshan Zhong, Zhongzhan Huang, Wushao Wen +2
Diffusion models, which have emerged to become popular text-to-image generation models, can produce high-quality and content-rich images guided by textual prompts. However, there a…
ASR: Attention-alike Structural Re-parameterization
Shanshan Zhong, Zhongzhan Huang, Wushao Wen +2
The structural re-parameterization (SRP) technique is a novel deep learning technique that achieves interconversion between different network architectures through equivalent param…
Deepening Neural Networks Implicitly and Locally via Recurrent Attention Strategy
Shanshan Zhong, Wushao Wen, Jinghui Qin +1
More and more empirical and theoretical evidence shows that deepening neural networks can effectively improve their performance under suitable training settings. However, deepening…
Switchable Self-attention Module
Shanshan Zhong, Wushao Wen, Jinghui Qin
Attention mechanism has gained great success in vision recognition. Many works are devoted to improving the effectiveness of attention mechanism, which finely design the structure…
Mix-Pooling Strategy for Attention Mechanism
Shanshan Zhong, Wushao Wen, Jinghui Qin
Recently many effective attention modules are proposed to boot the model performance by exploiting the internal information of convolutional neural networks in computer vision. In…
Difficulty-aware Image Super Resolution via Deep Adaptive Dual-Network
Jinghui Qin, Ziwei Xie, Yukai Shi +1
Recently, deep learning based single image super-resolution(SR) approaches have achieved great development. The state-of-the-art SR methods usually adopt a feed-forward pipeline to…
PIRM Challenge on Perceptual Image Enhancement on Smartphones: Report
Andrey Ignatov, Radu Timofte, Thang Van Vu +45
This paper reviews the first challenge on efficient perceptual image enhancement with the focus on deploying deep learning models on smartphones. The challenge consisted of two tra…