1 code implementation • 20 Oct 2023 • Chang Shu, Jiuzhou Han, Fangyu Liu, Ehsan Shareghi, Nigel Collier
Embodied language comprehension emphasizes that language understanding is not solely a matter of mental processing in the brain but also involves interactions with the physical and social environment.
no code implementations • 9 Oct 2023 • Baian Chen, Chang Shu, Ehsan Shareghi, Nigel Collier, Karthik Narasimhan, Shunyu Yao
Recent efforts have augmented language models (LMs) with external tools or environments, leading to the development of language agents that can reason and act.
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1 code implementation • 25 Jul 2023 • Kaixin Zhang, Hongzhi Wang, Yabin Lu, ZiQi Li, Chang Shu, Yu Yan, Donghua Yang
Although both data-driven and hybrid methods are proposed to avoid this problem, most of them suffer from high training and estimation costs, limited scalability, instability, and long-tail distribution problems on high-dimensional tables, which seriously affects the practical application of learned cardinality estimators.
1 code implementation • 24 May 2023 • MinJe Choi, Jiaxin Pei, Sagar Kumar, Chang Shu, David Jurgens
Large language models (LLMs) have been shown to perform well at a variety of syntactic, discourse, and reasoning tasks.
no code implementations • 11 May 2022 • Chang Shu
Annotation noise is widespread in datasets, but manually revising a flawed corpus is time-consuming and error-prone.
no code implementations • 29 Apr 2022 • Chang Shu, Ziming Chen, Lei Chen, Kuan Ma, Minghui Wang, Haibing Ren
To the best of our knowledge, this is the first work to show that transformer-based networks can attain state-of-the-art performance in real-time in the single image depth estimation field.
no code implementations • 8 Mar 2022 • Xi Weng, Yan Yan, Genshun Dong, Chang Shu, Biao Wang, Hanzi Wang, Ji Zhang
This shows that DMA-Net provides a good tradeoff between segmentation quality and speed for semantic segmentation in street scenes.
1 code implementation • 18 Nov 2021 • Xiang Bai, Hanchen Wang, Liya Ma, Yongchao Xu, Jiefeng Gan, Ziwei Fan, Fan Yang, Ke Ma, Jiehua Yang, Song Bai, Chang Shu, Xinyu Zou, Renhao Huang, Changzheng Zhang, Xiaowu Liu, Dandan Tu, Chuou Xu, Wenqing Zhang, Xi Wang, Anguo Chen, Yu Zeng, Dehua Yang, Ming-Wei Wang, Nagaraj Holalkere, Neil J. Halin, Ihab R. Kamel, Jia Wu, Xuehua Peng, Xiang Wang, Jianbo Shao, Pattanasak Mongkolwat, Jianjun Zhang, Weiyang Liu, Michael Roberts, Zhongzhao Teng, Lucian Beer, Lorena Escudero Sanchez, Evis Sala, Daniel Rubin, Adrian Weller, Joan Lasenby, Chuangsheng Zheng, Jianming Wang, Zhen Li, Carola-Bibiane Schönlieb, Tian Xia
Artificial intelligence (AI) provides a promising substitution for streamlining COVID-19 diagnoses.
no code implementations • 11 Aug 2021 • Zijian Zhang, Chang Shu, Youxin Chen, Jing Xiao, Qian Zhang, Lu Zheng
Integrating multimodal knowledge for abstractive summarization task is a work-in-progress research area, with present techniques inheriting fusion-then-generation paradigm.
1 code implementation • Findings (ACL) 2021 • Chang Shu, Yusen Zhang, Xiangyu Dong, Peng Shi, Tao Yu, Rui Zhang
Text generation from semantic parses is to generate textual descriptions for formal representation inputs such as logic forms and SQL queries.
1 code implementation • ECCV 2020 • Chang Shu, Kun Yu, Zhixiang Duan, Kuiyuan Yang
Photometric loss is widely used for self-supervised depth and egomotion estimation.
no code implementations • 11 May 2020 • Chang Shu, Xi Chen, Qiwei Xie, Chi Xiao, Hua Han
In this paper, we propose a novel non-iterative algorithm to simultaneously estimate optimal rigid transformation for serial section images, which is a key component in volume reconstruction of serial sections of biological tissue.
1 code implementation • 2 Apr 2020 • Jey Han Lau, Carlos S. Armendariz, Shalom Lappin, Matthew Purver, Chang Shu
We study the influence of context on sentence acceptability.
no code implementations • TACL 2020 • Jey Han Lau, Carlos Armendariz, Shalom Lappin, Matthew Purver, Chang Shu
We study the influence of context on sentence acceptability.
no code implementations • NAACL 2019 • Kaimin Zhou, Chang Shu, Binyang Li, Jey Han Lau
Motivated by this, our paper focuses on the task of rumour detection; particularly, we are interested in understanding how early we can detect them.
no code implementations • 5 Mar 2018 • Pengcheng Xi, Chang Shu, Rafik Goubran
State-of-the-art deep convolutional neural networks are compared on their performance of classifying the abnormalities.
no code implementations • 29 Jan 2018 • Chang Shu, Xi Chen, Qiwei Xie, Hua Han
Computer vision researchers have been expecting that neural networks have spatial transformation ability to eliminate the interference caused by geometric distortion for a long time.
no code implementations • 17 Dec 2013 • Stefanie Wuhrer, Leonid Pishchulin, Alan Brunton, Chang Shu, Jochen Lang
Our method can estimate the body shape and posture of both static scans and motion sequences of dressed human body scans.
no code implementations • 19 Jun 2013 • Stefanie Wuhrer, Jochen Lang, Motahareh Tekieh, Chang Shu
Our method combines the use of prior information on the geometry of the object modeled by a smooth template and the use of a linear finite element method to predict the deformation.
no code implementations • 7 Feb 2012 • Augusto Salazar, Stefanie Wuhrer, Chang Shu, Flavio Prieto
The predicted landmarks are then used to compute point-to-point correspondences between a template model and the newly available scan.