Rˆ3Net:Relation-embedded Representation Reconstruction Network for Change Captioning

1 code implementation • EMNLP 2021 • Yunbin Tu, Liang Li, Chenggang Yan, Shengxiang Gao, Zhengtao Yu

In this paper, we propose a Relation-embedded Representation Reconstruction Network (Rˆ3Net) to explicitly distinguish the real change from the large amount of clutter and irrelevant changes.

Semantic Relation-aware Difference Representation Learning for Change Captioning

1 code implementation • Findings (ACL) 2021 • Yunbin Tu, Tingting Yao, Liang Li, Jiedong Lou, Shengxiang Gao, Zhengtao Yu, Chenggang Yan

Representation Learning

Neighborhood Contrastive Transformer for Change Captioning

1 code implementation • 6 Mar 2023 • Yunbin Tu, Liang Li, Li Su, Ke Lu, Qingming Huang

Change captioning is to describe the semantic change between a pair of similar images in natural language.

Image Captioning

R$^3$Net:Relation-embedded Representation Reconstruction Network for Change Captioning

1 code implementation • 20 Oct 2021 • Yunbin Tu, Liang Li, Chenggang Yan, Shengxiang Gao, Zhengtao Yu

In this paper, we propose a Relation-embedded Representation Reconstruction Network (R$^3$Net) to explicitly distinguish the real change from the large amount of clutter and irrelevant changes.