1 code implementation • 24 Oct 2024 • Yuang Ai, Xiaoqiang Zhou, Huaibo Huang, Xiaotian Han, Zhengyu Chen, Quanzeng You, Hongxia Yang
Our second contribution, DreamClear, is a DiT-based image restoration model.
no code implementations • 3 Jun 2024 • Xiang Wang, Shiwei Zhang, Changxin Gao, Jiayu Wang, Xiaoqiang Zhou, Yingya Zhang, Luxin Yan, Nong Sang
First, to reduce the optimization difficulty and ensure temporal coherence, we map the reference image along with the posture guidance and noise video into a common feature space by incorporating a unified video diffusion model.
no code implementations • CVPR 2024 • Yuang Ai, Huaibo Huang, Xiaoqiang Zhou, Jiexiang Wang, Ran He
Extensive experiments on 16 IR tasks underscore the superiority of MPerceiver in terms of adaptiveness generalizability and fidelity.
no code implementations • 5 Dec 2023 • Yuang Ai, Huaibo Huang, Xiaoqiang Zhou, Jiexiang Wang, Ran He
Extensive experiments on 16 IR tasks underscore the superiority of MPerceiver in terms of adaptiveness, generalizability and fidelity.
1 code implementation • NeurIPS 2023 • Qihang Fan, Huaibo Huang, Xiaoqiang Zhou, Ran He
This paper proposes a Fully Adaptive Self-Attention (FASA) mechanism for vision transformer to model the local and global information as well as the bidirectional interaction between them in context-aware ways.
1 code implementation • CVPR 2024 • Yuang Ai, Xiaoqiang Zhou, Huaibo Huang, Lei Zhang, Ran He
Unsupervised Domain Adaptation (UDA) can effectively address domain gap issues in real-world image Super-Resolution (SR) by accessing both the source and target data.
no code implementations • ICCV 2023 • Xiaoqiang Zhou, Huaibo Huang, Ran He, Zilei Wang, Jie Hu, Tieniu Tan
In particular, self-attention with cross-scale matching and convolution filters with different kernel sizes are designed to exploit the multi-scale features in images.
1 code implementation • CVPR 2023 • Huaibo Huang, Xiaoqiang Zhou, Jie Cao, Ran He, Tieniu Tan
STA decomposes vanilla global attention into multiplications of a sparse association map and a low-dimensional attention, leading to high efficiency in capturing global dependencies.
no code implementations • 20 Dec 2021 • Xin Ma, Xiaoqiang Zhou, Huaibo Huang, Gengyun Jia, Zhenhua Chai, Xiaolin Wei
This multi-scale architecture is beneficial for the decoder to utilize discriminative representations learned from encoders into images.
no code implementations • 29 Oct 2020 • Xin Ma, Xiaoqiang Zhou, Huaibo Huang, Zhenhua Chai, Xiaolin Wei, Ran He
It is difficult for encoders to capture such powerful representations under this complex situation.
1 code implementation • COLING 2016 • Yang Xiang, Xiaoqiang Zhou, Qingcai Chen, Zhihui Zheng, Buzhou Tang, Xiaolong Wang, Yang Qin
In community question answering (cQA), the quality of answers are determined by the matching degree between question-answer pairs and the correlation among the answers.
no code implementations • IJCNLP 2015 • Xiaoqiang Zhou, Baotian Hu, Qingcai Chen, Buzhou Tang, Xiaolong Wang
In this paper, the answer selection problem in community question answering (CQA) is regarded as an answer sequence labeling task, and a novel approach is proposed based on the recurrent architecture for this problem.