101 citations · 210 across the 12 of their papers we have counts for
12 papers
Benchmarking Badminton Action Recognition with a New Fine-Grained Dataset
Qi Li, Tzu-Chen Chiu, Hsiang-Wei Huang +2
In the dynamic and evolving field of computer vision, action recognition has become a key focus, especially with the advent of sophisticated methodologies like Convolutional Neural…
Dynamic Graph Representation Learning for Depression Screening with Transformer
Ai-Te Kuo, Haiquan Chen, Yu-Hsuan Kuo +1
Early detection of mental disorder is crucial as it enables prompt intervention and treatment, which can greatly improve outcomes for individuals suffering from debilitating mental…
Rethinking Graph Lottery Tickets: Graph Sparsity Matters
Bo Hui, Da Yan, Xiaolong Ma +1
Lottery Ticket Hypothesis (LTH) claims the existence of a winning ticket (i.e., a properly pruned sub-network together with original weight initialization) that can achieve competi…
Multi-modal Transformer Path Prediction for Autonomous Vehicle
Chia Hong Tseng, Jie Zhang, Min-Te Sun +2
Reasoning about vehicle path prediction is an essential and challenging problem for the safe operation of autonomous driving systems. There exist many research works for path predi…
RFID-Based Indoor Spatial Query Evaluation with Bayesian Filtering Techniques
Bo Hui, Wenlu Wang, Jiao Yu +4
People spend a significant amount of time in indoor spaces (e.g., office buildings, subway systems, etc.) in their daily lives. Therefore, it is important to develop efficient indo…
MGC: A Complex-Valued Graph Convolutional Network for Directed Graphs
Jie Zhang, Bo Hui, Po-Wei Harn +2
Recent advancements in Graph Neural Networks have led to state-of-the-art performance on graph representation learning. However, the majority of existing works process directed gra…
Deep Multi-attribute Graph Representation Learning on Protein Structures
Tian Xia, Wei-Shinn Ku
Graphs as a type of data structure have recently attracted significant attention. Representation learning of geometric graphs has achieved great success in many fields including mo…
SpatialNLI: A Spatial Domain Natural Language Interface to Databases Using Spatial Comprehension
Jingjing Li, Wenlu Wang, Wei-Shinn Ku +2
A natural language interface (NLI) to databases is an interface that translates a natural language question to a structured query that is executable by database management systems…
Strike (with) a Pose: Neural Networks Are Easily Fooled by Strange Poses of Familiar Objects
Michael A. Alcorn, Qi Li, Zhitao Gong +4
Despite excellent performance on stationary test sets, deep neural networks (DNNs) can fail to generalize to out-of-distribution (OoD) inputs, including natural, non-adversarial on…
A Transfer-Learnable Natural Language Interface for Databases
Wenlu Wang, Yingtao Tian, Hongyu Xiong +2
Relational database management systems (RDBMSs) are powerful because they are able to optimize and answer queries against any relational database. A natural language interface (NLI…
Adversarial Texts with Gradient Methods
Zhitao Gong, Wenlu Wang, Bo Li +2
Adversarial samples for images have been extensively studied in the literature. Among many of the attacking methods, gradient-based methods are both effective and easy to compute.…
Adversarial and Clean Data Are Not Twins
Zhitao Gong, Wenlu Wang, Wei-Shinn Ku
Adversarial attack has cast a shadow on the massive success of deep neural networks. Despite being almost visually identical to the clean data, the adversarial images can fool deep…