Search Results for author: Jun Tang

Found 9 papers, 3 papers with code

Sequential Model for Predicting Patient Adherence in Subcutaneous Immunotherapy for Allergic Rhinitis

1 code implementation21 Jan 2024 Yin Li, Yu Xiong, Wenxin Fan, Kai Wang, Qingqing Yu, Liping Si, Patrick van der Smagt, Jun Tang, Nutan Chen

Conclusion: We creatively apply sequential models in the long-term management of SCIT with promising accuracy in the prediction of SCIT nonadherence in Allergic Rhinitis (AR) patients.

Management

PTSEFormer: Progressive Temporal-Spatial Enhanced TransFormer Towards Video Object Detection

1 code implementation6 Sep 2022 Han Wang, Jun Tang, Xiaodong Liu, Shanyan Guan, Rong Xie, Li Song

The temporal information is introduced by the temporal feature aggregation model (TFAM), by conducting an attention mechanism between the context frames and the target frame (i. e., the frame to be detected).

object-detection Video Object Detection

Vision-Language Pre-Training for Boosting Scene Text Detectors

2 code implementations CVPR 2022 Sibo Song, Jianqiang Wan, Zhibo Yang, Jun Tang, Wenqing Cheng, Xiang Bai, Cong Yao

In this paper, we specifically adapt vision-language joint learning for scene text detection, a task that intrinsically involves cross-modal interaction between the two modalities: vision and language, since text is the written form of language.

Contrastive Learning Language Modelling +4

MOST: A Multi-Oriented Scene Text Detector with Localization Refinement

no code implementations CVPR 2021 Minghang He, Minghui Liao, Zhibo Yang, Humen Zhong, Jun Tang, Wenqing Cheng, Cong Yao, Yongpan Wang, Xiang Bai

Over the past few years, the field of scene text detection has progressed rapidly that modern text detectors are able to hunt text in various challenging scenarios.

Scene Text Detection Text Detection

Temporally Object-based Video Co-Segmentation

no code implementations9 Feb 2018 Michael Ying Yang, Matthias Reso, Jun Tang, Wentong Liao, Bodo Rosenhahn

Therefore, we formulate a graphical model to select a proposal stream for each object in which the pairwise potentials consist of the appearance dissimilarity between different streams in the same video and also the similarity between the streams in different videos.

Object Segmentation

Privacy Loss in Apple's Implementation of Differential Privacy on MacOS 10.12

no code implementations8 Sep 2017 Jun Tang, Aleksandra Korolova, Xiaolong Bai, Xueqiang Wang, Xiao-Feng Wang

We discover and describe Apple's set-up for differentially private data processing, including the overall data pipeline, the parameters used for differentially private perturbation of each piece of data, and the frequency with which such data is sent to Apple's servers.

Management

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