Linguistic Structure Guided Context Modeling for Referring Image Segmentation
This work addresses the challenge of accurately segmenting objects based on natural language descriptions, which is important for applications in computer vision and human-computer interaction, and represents an incremental improvement over existing methods.
The paper tackled the problem of insufficient or redundant multimodal context modeling in referring image segmentation by proposing a Linguistic Structure guided Context Modeling (LSCM) module, which outperformed all previous state-of-the-art methods on four benchmarks.
Referring image segmentation aims to predict the foreground mask of the object referred by a natural language sentence. Multimodal context of the sentence is crucial to distinguish the referent from the background. Existing methods either insufficiently or redundantly model the multimodal context. To tackle this problem, we propose a "gather-propagate-distribute" scheme to model multimodal context by cross-modal interaction and implement this scheme as a novel Linguistic Structure guided Context Modeling (LSCM) module. Our LSCM module builds a Dependency Parsing Tree suppressed Word Graph (DPT-WG) which guides all the words to include valid multimodal context of the sentence while excluding disturbing ones through three steps over the multimodal feature, i.e., gathering, constrained propagation and distributing. Extensive experiments on four benchmarks demonstrate that our method outperforms all the previous state-of-the-arts.