Search Results for author: Pingxuan Huang

Found 5 papers, 2 papers with code

In-the-Wild Video Question Answering

no code implementations COLING 2022 Santiago Castro, Naihao Deng, Pingxuan Huang, Mihai Burzo, Rada Mihalcea

Existing video understanding datasets mostly focus on human interactions, with little attention being paid to the “in the wild” settings, where the videos are recorded outdoors.

Evidence Selection Question Answering +2

WildQA: In-the-Wild Video Question Answering

no code implementations14 Sep 2022 Santiago Castro, Naihao Deng, Pingxuan Huang, Mihai Burzo, Rada Mihalcea

Existing video understanding datasets mostly focus on human interactions, with little attention being paid to the "in the wild" settings, where the videos are recorded outdoors.

Evidence Selection Question Answering +2

Cross-domain Trajectory Prediction with CTP-Net

no code implementations22 Oct 2021 Pingxuan Huang, Zhenhua Cui, Jing Li, Shenghua Gao, Bo Hu, Yanyan Fang

Further, considering the consistency between the observed and the predicted trajectories, a target domain offset discriminator is utilized to adversarially regularize the future trajectory predictions to be in line with the observed trajectories.

Domain Adaptation Pedestrian Trajectory Prediction +1

FIBER: Fill-in-the-Blanks as a Challenging Video Understanding Evaluation Framework

1 code implementation ACL 2022 Santiago Castro, Ruoyao Wang, Pingxuan Huang, Ian Stewart, Oana Ignat, Nan Liu, Jonathan C. Stroud, Rada Mihalcea

We propose fill-in-the-blanks as a video understanding evaluation framework and introduce FIBER -- a novel dataset consisting of 28, 000 videos and descriptions in support of this evaluation framework.

Language Modelling Multiple-choice +4

FGraDA: A Dataset and Benchmark for Fine-Grained Domain Adaptation in Machine Translation

1 code implementation LREC 2022 Wenhao Zhu, ShuJian Huang, Tong Pu, Pingxuan Huang, Xu Zhang, Jian Yu, Wei Chen, Yanfeng Wang, Jiajun Chen

Previous research for adapting a general neural machine translation (NMT) model into a specific domain usually neglects the diversity in translation within the same domain, which is a core problem for domain adaptation in real-world scenarios.

Autonomous Vehicles Domain Adaptation +3

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