activity
20192021
most citedOCID-Ref: A 3D Robotic Dataset with Embodied Language for Clutter Scene Grounding

2 citations · 3 across the 3 of their papers we have counts for

collaborators

5 papers

cs.CL20212 cited

OCID-Ref: A 3D Robotic Dataset with Embodied Language for Clutter Scene Grounding

Ke-Jyun Wang, Yun-Hsuan Liu, Hung-Ting Su +4

To effectively apply robots in working environments and assist humans, it is essential to develop and evaluate how visual grounding (VG) can affect machine performance on occluded…

cs.MM2021

End-to-End Video Question-Answer Generation with Generator-Pretester Network

Hung-Ting Su, Chen-Hsi Chang, Po-Wei Shen +5

We study a novel task, Video Question-Answer Generation (VQAG), for challenging Video Question Answering (Video QA) task in multimedia. Due to expensive data annotation costs, many…

cs.CL20211 cited

Situation and Behavior Understanding by Trope Detection on Films

Chen-Hsi Chang, Hung-Ting Su, Jui-heng Hsu +7

The human ability of deep cognitive skills are crucial for the development of various real-world applications that process diverse and abundant user generated input. While recent p…

cs.CL2020

Investigating the Decoders of Maximum Likelihood Sequence Models: A Look-ahead Approach

Yu-Siang Wang, Yen-Ling Kuo, Boris Katz

We demonstrate how we can practically incorporate multi-step future information into a decoder of maximum likelihood sequence models. We propose a "k-step look-ahead" module to con…

cs.CV2019

Video Question Generation via Cross-Modal Self-Attention Networks Learning

Yu-Siang Wang, Hung-Ting Su, Chen-Hsi Chang +2

We introduce a novel task, Video Question Generation (Video QG). A Video QG model automatically generates questions given a video clip and its corresponding dialogues. Video QG req…