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Zhiyu Chen, Mohamed Trabelsi, Jeff Heflin, Dawei Yin, Brian D. Davison
2021 Proceedings of the 30th ACM International Conference on Information & Knowledge Management  
In this paper, we propose to model the complex relations in the table corpus as one or more graphs and then utilize graph neural networks to learn representations of queries and tables.  ...  Table search aims to retrieve a list of tables given a user's query. Previous methods only consider the textual information of tables and the structural information is rarely used.  ...  METHOD In this section, we introduce the details of our proposed Multi-Graph NEural networks for Table Search (MGNETS for short).  ... 
doi:10.1145/3459637.3482140 fatcat:4oy4qhbflnd3npwiykpyvvefme

Accelerating numerical methods by gradient-based meta-solving [article]

Sohei Arisaka, Qianxiao Li
2022 arXiv   pre-print
As an illustration of this approach, we study the adaptive generation of parameters for iterative solvers to accelerate the solution of differential equations.  ...  To date, a variety of such domain-specific methods have been proposed in the literature, but a generic approach for designing these methods remains under-explored.  ...  This work presents Neural A* search, a data-driven search method for path planning problems.  ... 
arXiv:2206.08594v1 fatcat:lyagujh4lbbytpzllzqvmemjvi

A two‐scaled fully convolutional learning network for road detection

Dingding Yu, Xianliang Hu, Kewei Liang
2021 IET Image Processing  
This paper aims to detect road regions based on a two-scaled deep neural network.  ...  It enables to detect the road areas by multi-scale feature maps from different reception fields.  ...  The multi-scale strategies are widely applied in many computational problems, as well as in network design for deep learning.  ... 
doi:10.1049/ipr2.12157 fatcat:pkupdnladnhurginrbh5j3jopq

Deep Learning for Visual Tracking: A Comprehensive Survey [article]

Seyed Mojtaba Marvasti-Zadeh, Li Cheng, Hossein Ghanei-Yakhdan, and Shohreh Kasaei
2019 arXiv   pre-print
First, the fundamental characteristics, primary motivations, and contributions of DL-based methods are summarized from six key aspects of: network architecture, network exploitation, network training for  ...  visual tracking, network objective, network output, and the exploitation of correlation filter advantages.  ...  Kamal Nasrollahi (Visual Analysis of People Lab (VAP), Aalborg University) for his beneficial comments.  ... 
arXiv:1912.00535v1 fatcat:v5ikqi2cpbblhgtkiu6z6l5anq

Recent Advancements in Fruit Detection and Classification Using Deep Learning Techniques

Chiagoziem C. Ukwuoma, Qin Zhiguang, Md Belal Bin Heyat, Liaqat Ali, Zahra Almaspoor, Happy N. Monday, Dost Muhammad Khan
2022 Mathematical Problems in Engineering  
For the agri-food industry, the use of advanced technology is essential.  ...  Additionally, we also implemented from scratch a deep learning model for fruit classification using the popular dataset "Fruit 360" to make it easier for beginner researchers in the field of agriculture  ...  We acknowledge the Key Laboratory of Network and Data Security, University of Electronic Science and Technology of China, and IoT Research Center, Shenzhen University, China, for providing better environment  ... 
doi:10.1155/2022/9210947 fatcat:tm4ivc3pbrc5npthxs7oicquwa

DeepPanoContext: Panoramic 3D Scene Understanding with Holistic Scene Context Graph and Relation-based Optimization [article]

Cheng Zhang, Zhaopeng Cui, Cai Chen, Shuaicheng Liu, Bing Zeng, Hujun Bao, Yinda Zhang
2021 arXiv   pre-print
In order to fully utilize the rich context information, we design a novel graph neural network based context model to predict the relationship among objects and room layout, and a differentiable relationship-based  ...  In this paper, we propose a novel method for panoramic 3D scene understanding which recovers the 3D room layout and the shape, pose, position, and semantic category for each object from a single full-view  ...  [47] improves the performance of all three tasks via the implicit function and scene graph neural network.  ... 
arXiv:2108.10743v1 fatcat:knc6p65etvfihcb6rey3oihrfu