no code implementations • 17 Dec 2024 • Ruijie Chen, Qi Mao, Zhengxue Cheng
Recent advances in Artificial Intelligence Generated Content (AIGC) have garnered significant interest, accompanied by an increasing need to transmit and compress the vast number of AI-generated images (AIGIs).
no code implementations • 23 Sep 2024 • Yuanhang Li, Qi Mao, Lan Chen, Zhen Fang, Lei Tian, Xinyan Xiao, Libiao Jin, Hua Wu
To enhance the motion-subject binding, we implement a syntax-guided contrastive constraint in the subsequent denoising phase, aimed at improving the correlations between the CA maps of verbs and their corresponding nouns. Both qualitative and quantitative evaluations demonstrate that the proposed framework significantly outperforms baseline approaches, producing higher-quality videos with improved subject-motion consistency.
1 code implementation • 22 Aug 2024 • Lingyu Zhu, Wenhan Yang, Baoliang Chen, Hanwei Zhu, Zhangkai Ni, Qi Mao, Shiqi Wang
To address the above challenge, we propose the Unrolled Decomposed Unpaired Network (UDU-Net) for enhancing low-light videos by unrolling the optimization functions into a deep network to decompose the signal into spatial and temporal-related factors, which are updated iteratively.
no code implementations • 6 Mar 2024 • Naifu Xue, Qi Mao, Zijian Wang, Yuan Zhang, Siwei Ma
Recent progress in generative compression technology has significantly improved the perceptual quality of compressed data.
no code implementations • 25 Dec 2023 • Qi Mao, Chongyu Wang, Meng Wang, Shiqi Wang, Ruijie Chen, Libiao Jin, Siwei Ma
The accelerated proliferation of visual content and the rapid development of machine vision technologies bring significant challenges in delivering visual data on a gigantic scale, which shall be effectively represented to satisfy both human and machine requirements.
no code implementations • 18 Dec 2023 • Qi Mao, Lan Chen, YuChao Gu, Zhen Fang, Mike Zheng Shou
Recent diffusion-based image editing approaches have exhibited impressive editing capabilities in images with simple compositions.
no code implementations • 17 Jul 2023 • Qi Mao, Tinghan Yang, Yinuo Zhang, Zijian Wang, Meng Wang, Shiqi Wang, Siwei Ma
Remarkably, even with the loss of up to $20\%$ of indices, the images can be effectively restored with minimal perceptual loss.
2 code implementations • 10 Nov 2020 • Jianhui Chang, Zhenghui Zhao, Chuanmin Jia, Shiqi Wang, Lingbo Yang, Qi Mao, Jian Zhang, Siwei Ma
To this end, we propose a novel conceptual compression framework that encodes visual data into compact structure and texture representations, then decodes in a deep synthesis fashion, aiming to achieve better visual reconstruction quality, flexible content manipulation, and potential support for various vision tasks.
1 code implementation • 2 Nov 2020 • Qi Mao, Hung-Yu Tseng, Hsin-Ying Lee, Jia-Bin Huang, Siwei Ma, Ming-Hsuan Yang
Generating a smooth sequence of intermediate results bridges the gap of two different domains, facilitating the morphing effect across domains.
4 code implementations • 2 May 2019 • Hsin-Ying Lee, Hung-Yu Tseng, Qi Mao, Jia-Bin Huang, Yu-Ding Lu, Maneesh Singh, Ming-Hsuan Yang
In this work, we present an approach based on disentangled representation for generating diverse outputs without paired training images.
2 code implementations • CVPR 2019 • Qi Mao, Hsin-Ying Lee, Hung-Yu Tseng, Siwei Ma, Ming-Hsuan Yang
In this work, we propose a simple yet effective regularization term to address the mode collapse issue for cGANs.
Diversity
Multimodal Unsupervised Image-To-Image Translation
+1
no code implementations • 9 Dec 2015 • Qi Mao, Li Wang, Ivor W. Tsang, Yijun Sun
As showcases, models that can learn a spanning tree or a weighted undirected $\ell_1$ graph are proposed, and a new learning algorithm is developed that learns a set of principal points and a graph structure from data, simultaneously.
no code implementations • 5 Mar 2011 • Qi Mao, Ivor W. Tsang
The analyses by comparing with the state-of-the-art feature selection methods show that the proposed method is superior to others.
no code implementations • 4 Mar 2011 • Qi Mao, Ivor W. Tsang
To alleviate this issue, in this paper, we propose a novel multiple template learning paradigm to learn structured prediction and the importance of each template simultaneously, so that hundreds of arbitrary templates could be added into the learning model without caution.