no code implementations • 13 Mar 2024 • Qing Lin, Jingfeng Zhang, Yew Soon Ong, Mengmi Zhang
For the first time, we present a novel challenge of emotion-evoked image generation, aiming to synthesize images that evoke target emotions while retaining the semantics and structures of the original scenes.
no code implementations • 4 May 2023 • Xuhao Jiang, Weimin Tan, Qing Lin, Chenxi Ma, Bo Yan, Liquan Shen
In recent years, many convolutional neural network-based models are designed for JPEG artifacts reduction, and have achieved notable progress.
no code implementations • 14 Jul 2022 • Chenxi Ma, Bo Yan, Qing Lin, Weimin Tan, Siming Chen
To enhance the semantic accuracy and the visual quality of the reconstructed image, we explore the multi-modal fusion learning in SISR by proposing a Text-Guided Super-Resolution (TGSR) framework, which can effectively utilize the information from the text and image modalities.
no code implementations • 12 May 2021 • Qing Lin, Yongbin Liu, Wen Wen, Zhihua Tao
It is difficult for a single model to adapt to various relation learning, which results in the high variance problem.
no code implementations • 6 Jul 2020 • Dae Heun Koh, Pierre Côte de Soux, Laura Dominé, François Drielsma, Ran Itay, Qing Lin, Kazuhiro Terao, Ka Vang Tsang, Tracy Usher
This work contributes to the development of an end-to-end optimizable full data reconstruction chain for LArTPCs, in particular pixel-based 3D imaging detectors including the near detector of the Deep Underground Neutrino Experiment.
no code implementations • 2 Jul 2020 • Francois Drielsma, Qing Lin, Pierre Côte de Soux, Laura Dominé, Ran Itay, Dae Heun Koh, Bradley J. Nelson, Kazuhiro Terao, Ka Vang Tsang, Tracy L. Usher
The optimized algorithm is then applied to the related task of clustering particle instances into interactions and yields a mean ARI of 99. 2 % for an interaction density of $\sim\mathcal{O}(1)\, m^{-3}$.
no code implementations • 26 Jun 2020 • Laura Dominé, Pierre Côte de Soux, François Drielsma, Dae Heun Koh, Ran Itay, Qing Lin, Kazuhiro Terao, Ka Vang Tsang, Tracy L. Usher
Using as a benchmark the PILArNet public LArTPC data sample in which the voxel resolution is 3mm/voxel, our algorithm successfully predicted 96. 8% and 97. 8% of 3D points within a distance of 3 and 10~voxels from the provided true point locations respectively.