1 code implementation • 22 Oct 2023 • Chunlei Wang, Wenquan Feng, Xiangtai Li, Guangliang Cheng, Shuchang Lyu, Binghao Liu, Lijiang Chen, Qi Zhao
While current foundational models excel at various visual language tasks, there's a noticeable absence of models specifically tailored for open-vocabulary visual grounding.
1 code implementation • 13 Jan 2023 • Qi Zhao, Shuchang Lyu, Binghao Liu, Lijiang Chen, Hongbo Zhao
We first propose source student backbone and target student backbone to respectively extract the source-style and target-style feature for both source and target images.
no code implementations • 17 Aug 2022 • Menghao Li, Wenquan Feng, Shuchang Lyu, Lijiang Chen, Qi Zhao
On the DSB2018 and CA2. 5, our network surpasses previous methods by 1. 2% (AP50).
1 code implementation • 14 Jul 2022 • Qi Zhao, Shuchang Lyu, Wenpei Bai, Linghan Cai, Binghao Liu, Guangliang Cheng, Meijing Wu, Xiubo Sang, Min Yang, Lijiang Chen
To solve this problem, we propose a Multi-Modality Ovarian Tumor Ultrasound (MMOTU) image dataset containing 1469 2d ultrasound images and 170 contrast enhanced ultrasonography (CEUS) images with pixel-wise and global-wise annotations.
no code implementations • 29 Nov 2021 • Qi Zhao, YuFei Wang, Shuchang Lyu, Lijiang Chen
In this paper, we propose attention-based feature decomposition-reconstruction network for scene text detection, which utilizes contextual information and low-level feature to enhance the performance of segmentation-based text detector.
no code implementations • 1 Apr 2021 • Qi Zhao, Yujing Ma, Shuchang Lyu, Lijiang Chen
On this issue, we embed self-distillation (SD) method to transfer knowledge from ensemble network to main-branch in it.
no code implementations • 29 Dec 2020 • Qi Zhao, Shuchang Lyu, Yuewen Li, Yujing Ma, Lijiang Chen
To avoid the interference from confusing information, we propose Multi-granularity Multi-Level Feature Ensemble Module (MGML-FEM) which can provide diverse predictions by full-channel feature generator (FC-FG).
no code implementations • 22 Jan 2015 • Xingyu Wu, Xia Mao, Lijiang Chen, Yuli Xue, Angelo Compare
Secondly, the shape context is nonlinearly mapped to a subspace by kernel nonparametric discriminant analysis (KNDA) to get a compact feature representation, and thus a trajectory is projected to a single point in a low-dimensional feature space.
no code implementations • 22 May 2014 • Lijiang Chen
Although the parallel corpus has an irreplaceable role in machine translation, its scale and coverage is still beyond the actual needs.