2 code implementations • 15 Dec 2023 • Kung-Hsiang Huang, Mingyang Zhou, Hou Pong Chan, Yi R. Fung, Zhenhailong Wang, Lingyu Zhang, Shih-Fu Chang, Heng Ji
This work inaugurates a new domain in factual error correction for chart captions, presenting a novel evaluation mechanism, and demonstrating an effective approach to ensuring the factuality of generated chart captions.
Factual Inconsistency Detection in Chart Captioning Image Captioning +1
no code implementations • 2 Dec 2023 • Lingyu Zhang, Ting Hua, Yilin Shen, Hongxia Jin
In order to achieve this goal, a model has to be "smart" and "knowledgeable".
no code implementations • 12 Apr 2023 • James Seale Smith, Yen-Chang Hsu, Lingyu Zhang, Ting Hua, Zsolt Kira, Yilin Shen, Hongxia Jin
We show that C-LoRA not only outperforms several baselines for our proposed setting of text-to-image continual customization, which we refer to as Continual Diffusion, but that we achieve a new state-of-the-art in the well-established rehearsal-free continual learning setting for image classification.
no code implementations • 13 Jan 2023 • Hongjun Wang, Zhiwen Zhang, Zipei Fan, Jiyuan Chen, Lingyu Zhang, Ryosuke Shibasaki, Xuan Song
Subsequently, a Multitask Weakly Supervised Learning Framework for Travel Time Estimation (MWSL TTE) has been proposed to infer transition probability between roads segments, and the travel time on road segments and intersection simultaneously.
no code implementations • 13 Dec 2022 • Lingyu Zhang, Chengzhi Mao, Junfeng Yang, Carl Vondrick
Even under adaptive attacks where the adversary knows our defense, our algorithm is still effective.
1 code implementation • 12 Dec 2022 • Chengzhi Mao, Lingyu Zhang, Abhishek Joshi, Junfeng Yang, Hao Wang, Carl Vondrick
In this paper, we introduce a framework that uses the dense intrinsic constraints in natural images to robustify inference.
2 code implementations • 28 Nov 2022 • Hongjun Wang, Jiyuan Chen, Tong Pan, Zipei Fan, Boyuan Zhang, Renhe Jiang, Lingyu Zhang, Yi Xie, Zhongyi Wang, Xuan Song
Spatial-temporal (ST) graph modeling, such as traffic speed forecasting and taxi demand prediction, is an important task in deep learning area.
no code implementations • 26 Oct 2021 • Huichen Ma, Junjie Zhou, Jian Zhang, Lingyu Zhang
After training with sample data, the BP neural network model can represent the relation between the manipulator tip position and the pressure applied to the chambers.
1 code implementation • 7 May 2021 • Lingyu Zhang, Tianyu Liu, Yunhai Wang
In addition, to the numerical solution of the manpower scheduling problem, this paper also studies the algorithm for scheduling task list generation and the method of displaying scheduling results.
1 code implementation • 7 May 2021 • Tianyu Liu, Lingyu Zhang
This paper proposes a new model combined with deep learning to solve the multi-shift manpower scheduling problem based on the existing research.
no code implementations • 7 Aug 2020 • Teng Ye, Wei Ai, Lingyu Zhang, Ning Luo, Lulu Zhang, Jieping Ye, Qiaozhu Mei
Through interpreting the best-performing models, we discover many novel and actionable insights regarding how to optimize the design and the execution of team competitions on ride-sharing platforms.
1 code implementation • 2020 IEEE 36th International Conference on Data Engineering (ICDE) 2020 • Hongzhi Shi, Quanming Yao, Qi Guo, Yaguang Li, Lingyu Zhang, Jieping Ye, Yong Li, Yan Liu
Predicting Origin-Destination (OD) flow is a crucial problem for intelligent transportation.
no code implementations • 25 Sep 2019 • Xu Geng, Lingyu Zhang, Shulin Li, Yuanbo Zhang, Lulu Zhang, Leye Wang, Qiang Yang, Hongtu Zhu, Jieping Ye
Deep learning based approaches have been widely used in various urban spatio-temporal forecasting problems, but most of them fail to account for the unsmoothness issue of urban data in their architecture design, which significantly deteriorates their prediction performance.
1 code implementation • AAAI 2019 • Xu Geng, Yaguang Li, Leye Wang, Lingyu Zhang, Qiang Yang, Jieping Ye, Yan Liu
This task is challenging due to the complicated spatiotemporal dependencies among regions.
no code implementations • ACL 2019 • Manling Li, Lingyu Zhang, Heng Ji, Richard J. Radke
Transcripts of natural, multi-person meetings differ significantly from documents like news articles, which can make Natural Language Generation models for generating summaries unfocused.
no code implementations • 27 May 2019 • Xu Geng, Xiyu Wu, Lingyu Zhang, Qiang Yang, Yan Liu, Jieping Ye
To incorporate multiple relationships into spatial feature extraction, we define the problem as a multi-modal machine learning problem on multi-graph convolution networks.
no code implementations • 18 Mar 2019 • Ji Zhao, Meiyu Yu, Huan Chen, Boning Li, Lingyu Zhang, Qi Song, Li Ma, Hua Chai, Jieping Ye
An accurate similarity calculation is challenging since the mismatch between a query and a retrieval text may exist in the case of a mistyped query or an alias inquiry.