no code implementations • CCL 2020 • Dinghe Xiao, Nannan Wang, Jiangang Yu, Chunhong Zhang, Jiaqi Wu
So we develop two pipelines of processing methods for semi-structured data and unstructured data respectively.
no code implementations • 19 Apr 2024 • Junbiao Pang, Baocheng Xiong, Jiaqi Wu
In this paper, we address these problems from a view that utilizes contexts of the cracks and propose an end-to-end deep learning method to model the context information flow.
no code implementations • 27 Mar 2024 • Jiaqi Wu, Junbiao Pang, Baochang Zhang, Qingming Huang
Semi-supervised learning (SSL) is a practical challenge in computer vision.
no code implementations • 21 Feb 2024 • Junbiao Pang, Tianyang Cai, Baochang Zhang, Jiaqi Wu, Ye Tao
Existing Quantization-Aware Training (QAT) methods intensively depend on the complete labeled dataset or knowledge distillation to guarantee the performances toward Full Precision (FP) accuracies.
1 code implementation • 3 Nov 2023 • Jiaqi Wu, Junbiao Pang, Qingming Huang
Both semi-supervised classification and regression are practically challenging tasks for computer vision.
no code implementations • 3 Nov 2023 • Jiaqi Wu, Junbiao Pang, Qingming Huang
Semi-supervised pose estimation is a practically challenging task for computer vision.
1 code implementation • 1 Dec 2022 • Yu Yuan, Jiaqi Wu, Zhongliang Jing, Henry Leung, Han Pan
In this letter, we present a hybrid model consisting of a convolutional encoder and a Transformer decoder to generate ghost-free HDR images.
no code implementations • 16 Nov 2022 • Yu Yuan, Jiaqi Wu, Lindong Wang, Zhongliang Jing, Henry Leung, Shuyuan Zhu, Han Pan
Capturing highly appreciated star field images is extremely challenging due to light pollution, the requirements of specialized hardware, and the high level of photographic skills needed.
1 code implementation • 18 Oct 2022 • Yu Yuan, Jiaqi Wu, Zhongliang Jing, Henry Leung, Han Pan
In this article, we present a hybrid model consisting of a convolutional encoder and a Transformer-based decoder to fuse multimodal images.
no code implementations • 27 Jan 2021 • Jiaqi Wu, Weihua Li, Quan Bai, Takayuki Ito, Ahmed Moustafa
A large amount of information has been published to online social networks every day.
no code implementations • 21 Nov 2020 • Vrindavan Harrison, Juraj Juraska, Wen Cui, Lena Reed, Kevin K. Bowden, Jiaqi Wu, Brian Schwarzmann, Abteen Ebrahimi, Rishi Rajasekaran, Nikhil Varghese, Max Wechsler-Azen, Steve Whittaker, Jeffrey Flanigan, Marilyn Walker
This report describes Athena, a dialogue system for spoken conversation on popular topics and current events.
no code implementations • 13 Aug 2019 • Kevin K. Bowden, Jiaqi Wu, Wen Cui, Juraj Juraska, Vrindavan Harrison, Brian Schwarzmann, Nicholas Santer, Steve Whittaker, Marilyn Walker
In contrast, search and general Chit-Chat induced coverage problems; here users found it hard to infer what topics SB could understand, with these conversations seen as being too system-driven.
no code implementations • 22 Jul 2019 • Kevin K. Bowden, Jiaqi Wu, Wen Cui, Juraj Juraska, Vrindavan Harrison, Brian Schwarzmann, Nick Santer, Marilyn Walker
One of the most interesting aspects of the Amazon Alexa Prize competition is that the framing of the competition requires the development of new computational models of dialogue and its structure.
1 code implementation • ACL 2019 • Mingyu Derek Ma, Kevin K. Bowden, Jiaqi Wu, Wen Cui, Marilyn Walker
Discourse relation identification has been an active area of research for many years, and the challenge of identifying implicit relations remains largely an unsolved task, especially in the context of an open-domain dialogue system.
Implicit Discourse Relation Classification Implicit Relations +2
no code implementations • 16 Feb 2019 • Jiaqi Wu, Ryan Compton, Geetanjali Rakshit, Marilyn Walker, Pranav Anand, Steve Whittaker
Our results indicate that generic characteristics are shared between the classes of agency, social and concepts, suggesting it should be possible to build general models for affective classification tasks.
no code implementations • LREC 2018 • Kevin K. Bowden, Jiaqi Wu, Shereen Oraby, Amita Misra, Marilyn Walker
In dialogue systems, the tasks of named entity recognition (NER) and named entity linking (NEL) are vital preprocessing steps for understanding user intent, especially in open domain interaction where we cannot rely on domain-specific inference.
no code implementations • 4 Jan 2018 • Kevin K. Bowden, Jiaqi Wu, Shereen Oraby, Amita Misra, Marilyn Walker
In this paper we introduce a novel, open domain socialbot for the Amazon Alexa Prize competition, aimed at carrying on friendly conversations with users on a variety of topics.
no code implementations • 15 Sep 2017 • Kevin K. Bowden, Shereen Oraby, Jiaqi Wu, Amita Misra, Marilyn Walker
The greatest challenges in building sophisticated open-domain conversational agents arise directly from the potential for ongoing mixed-initiative multi-turn dialogues, which do not follow a particular plan or pursue a particular fixed information need.
no code implementations • 10 Sep 2017 • Kevin K. Bowden, Shereen Oraby, Amita Misra, Jiaqi Wu, Stephanie Lukin
In order to build dialogue systems to tackle the ambitious task of holding social conversations, we argue that we need a data driven approach that includes insight into human conversational chit chat, and which incorporates different natural language processing modules.
no code implementations • ACL 2017 • Lena Reed, Jiaqi Wu, Shereen Oraby, Pranav Anand, Marilyn Walker
Informal first-person narratives are a unique resource for computational models of everyday events and people's affective reactions to them.
no code implementations • WS 2017 • Jiaqi Wu, Marilyn Walker, Pranav Anand, Steve Whittaker
Our goal is to ground the linguistic descriptions of events that users experience in theories of well-being and happiness, and then examine the extent to which different theoretical accounts can explain the variance in the happiness scores.
no code implementations • WS 2017 • Elahe Rahimtoroghi, Jiaqi Wu, Ruimin Wang, Pranav Anand, Marilyn A. Walker
Many genres of natural language text are narratively structured, a testament to our predilection for organizing our experiences as narratives.