Comprehensive Multi-Modal Interactions for Referring Image Segmentation

• URL: http://arxiv.org/abs/2104.10412v1
• Date: Wed, 21 Apr 2021 08:45:09 GMT
• Title: Comprehensive Multi-Modal Interactions for Referring Image Segmentation
• Authors: Kanishk Jain, Vineet Gandhi
• Abstract summary: We investigate Referring Image (RIS), which outputs a segmentation map corresponding to the given natural language description. To solve RIS efficiently, we need to understand each word's relationship with other words, each region in the image to other regions, and cross-modal alignment between linguistic and visual domains. We propose a Joint Reasoning (JRM) module and a novel Cross-Modal Multi-Level Fusion (CMMLF) module for tackling this task.
• Score: 7.064383217512461
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We investigate Referring Image Segmentation (RIS), which outputs a segmentation map corresponding to the given natural language description. To solve RIS efficiently, we need to understand each word's relationship with other words, each region in the image to other regions, and cross-modal alignment between linguistic and visual domains. Recent methods model these three types of interactions sequentially. We argue that such a modular approach limits these methods' performance, and joint simultaneous reasoning can help resolve ambiguities. To this end, we propose a Joint Reasoning (JRM) module and a novel Cross-Modal Multi-Level Fusion (CMMLF) module for tackling this task. JRM effectively models the referent's multi-modal context by jointly reasoning over visual and linguistic modalities (performing word-word, image region-region, word-region interactions in a single module). CMMLF module further refines the segmentation masks by exchanging contextual information across visual hierarchy through linguistic features acting as a bridge. We present thorough ablation studies and validate our approach's performance on four benchmark datasets, and show that the proposed method outperforms the existing state-of-the-art methods on all four datasets by significant margins.

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