Ronghang Hu


2021

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Proceedings of the Second Workshop on Advances in Language and Vision Research
Xin | Ronghang Hu | Drew Hudson | Tsu-Jui Fu | Marcus Rohrbach | Daniel Fried
Proceedings of the Second Workshop on Advances in Language and Vision Research

2020

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Proceedings of the First Workshop on Advances in Language and Vision Research
Xin Wang | Jesse Thomason | Ronghang Hu | Xinlei Chen | Peter Anderson | Qi Wu | Asli Celikyilmaz | Jason Baldridge | William Yang Wang
Proceedings of the First Workshop on Advances in Language and Vision Research

2019

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Are You Looking? Grounding to Multiple Modalities in Vision-and-Language Navigation
Ronghang Hu | Daniel Fried | Anna Rohrbach | Dan Klein | Trevor Darrell | Kate Saenko
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

Vision-and-Language Navigation (VLN) requires grounding instructions, such as “turn right and stop at the door”, to routes in a visual environment. The actual grounding can connect language to the environment through multiple modalities, e.g. “stop at the door” might ground into visual objects, while “turn right” might rely only on the geometric structure of a route. We investigate where the natural language empirically grounds under two recent state-of-the-art VLN models. Surprisingly, we discover that visual features may actually hurt these models: models which only use route structure, ablating visual features, outperform their visual counterparts in unseen new environments on the benchmark Room-to-Room dataset. To better use all the available modalities, we propose to decompose the grounding procedure into a set of expert models with access to different modalities (including object detections) and ensemble them at prediction time, improving the performance of state-of-the-art models on the VLN task.