Predicting and attending to damaging collisions for placing everyday objects in photo-realistic simulations

Magassouba, Aly; Sugiura, Komei; Nakayama, Angelica; Hirakawa, Tsubasa; Yamashita, Takayoshi; Fujiyoshi, Hironobu; Kawai, Hisashi

doi:10.1080/01691864.2021.1913446

Search citation statements

Order By: Relevance

Paper Sections

Select...

Introduction1

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2022

2024

Publication Types

Select...

Article3

Other2

Book1

Relationship

Self Cite1

Independent5

Authors

Journals

Cited by 8 publications

(1 citation statement)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…2. RELATED WORK There have been many studies in the field of caption generation [1,[4][5][6][7][8][9]. This field includes image captioning and video captioning.…”

Section: Introductionmentioning

confidence: 99%

Relational Future Captioning Model for Explaining Likely Collisions in Daily Tasks

Kambara¹,

Sugiura²

2022

Preprint

Self Cite

View full text Add to dashboard Cite

Domestic service robots that support daily tasks are a promising solution for elderly or disabled people. It is crucial for domestic service robots to explain the collision risk before they perform actions. In this paper, our aim is to generate a caption about a future event. We propose the Relational Future Captioning Model (RFCM), a crossmodal language generation model for the future captioning task. The RFCM has the Relational Self-Attention Encoder to extract the relationships between events more effectively than the conventional selfattention in transformers. We conducted comparison experiments, and the results show the RFCM outperforms a baseline method on two datasets.

show abstract

“…2. RELATED WORK There have been many studies in the field of caption generation [1,[4][5][6][7][8][9]. This field includes image captioning and video captioning.…”

Section: Introductionmentioning

confidence: 99%