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20222026
most citedWeakly-Supervised Multi-Granularity Map Learning for Vision-and-Language Navigation

16 citations · 36 across the 10 of their papers we have counts for

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7 papers · 1 filter

cs.CV2024

Fine-Grained Alignment in Vision-and-Language Navigation through Bayesian Optimization

Yuhang Song, Mario Gianni, Chenguang Yang +4

This paper addresses the challenge of fine-grained alignment in Vision-and-Language Navigation (VLN) tasks, where robots navigate realistic 3D environments based on natural languag…

cs.CV2024

MAGIC: Map-Guided Few-Shot Audio-Visual Acoustics Modeling

Diwei Huang, Kunyang Lin, Peihao Chen +2

Few-shot audio-visual acoustics modeling seeks to synthesize the room impulse response in arbitrary locations with few-shot observations. To sufficiently exploit the provided few-s…

cs.CV2023

Event-Guided Procedure Planning from Instructional Videos with Text Supervision

An-Lan Wang, Kun-Yu Lin, Jia-Run Du +2

In this work, we focus on the task of procedure planning from instructional videos with text supervision, where a model aims to predict an action sequence to transform the initial…

cs.CV2023

Learning Vision-and-Language Navigation from YouTube Videos

Kunyang Lin, Peihao Chen, Diwei Huang +3

Vision-and-language navigation (VLN) requires an embodied agent to navigate in realistic 3D environments using natural language instructions. Existing VLN methods suffer from train…

cs.CV2023★ 12 cited

DilateFormer: Multi-Scale Dilated Transformer for Visual Recognition

Jiayu Jiao, Yu-Ming Tang, Kun-Yu Lin +4

As a de facto solution, the vanilla Vision Transformers (ViTs) are encouraged to model long-range dependencies between arbitrary image patches while the global attended receptive f…

cs.CV2022★ 16 cited

Weakly-Supervised Multi-Granularity Map Learning for Vision-and-Language Navigation

Peihao Chen, Dongyu Ji, Kunyang Lin +4

We address a practical yet challenging problem of training robot agents to navigate in an environment following a path described by some language instructions. The instructions oft…