4 papers · 1 filter
Distractors-Immune Representation Learning with Cross-modal Contrastive Regularization for Change Captioning
Yunbin Tu, Liang Li, Li Su +2
Change captioning aims to succinctly describe the semantic change between a pair of similar images, while being immune to distractors (illumination and viewpoint changes). Under th…
Context-aware Difference Distilling for Multi-change Captioning
Yunbin Tu, Liang Li, Li Su +3
Multi-change captioning aims to describe complex and coupled changes within an image pair in natural language. Compared with single-change captioning, this task requires the model…
Self-supervised Cross-view Representation Reconstruction for Change Captioning
Yunbin Tu, Liang Li, Li Su +3
Change captioning aims to describe the difference between a pair of similar images. Its key challenge is how to learn a stable difference representation under pseudo changes caused…
Neighborhood Contrastive Transformer for Change Captioning
Yunbin Tu, Liang Li, Li Su +2
Change captioning is to describe the semantic change between a pair of similar images in natural language. It is more challenging than general image captioning, because it requires…