Multi-Modal Prototypes for Open-World Semantic Segmentation

TL;DR

This paper introduces a multi-modal prototype framework for open-world semantic segmentation, integrating visual and textual clues to improve generalization to unseen categories in a unified architecture.

Contribution

It proposes a novel multi-modal prototype-based segmentation method that decomposes language information into multiple aspects and fuses it with visual data for enhanced open-world segmentation.

Findings

Outperforms previous state-of-the-art on PASCAL-5^i and COCO-20^i datasets.

Effective in zero-shot, few-shot, and generalized segmentation tasks.

Ablation studies confirm the importance of each component.

Abstract

In semantic segmentation, generalizing a visual system to both seen categories and novel categories at inference time has always been practically valuable yet challenging. To enable such functionality, existing methods mainly rely on either providing several support demonstrations from the visual aspect or characterizing the informative clues from the textual aspect (e.g., the class names). Nevertheless, both two lines neglect the complementary intrinsic of low-level visual and high-level language information, while the explorations that consider visual and textual modalities as a whole to promote predictions are still limited. To close this gap, we propose to encompass textual and visual clues as multi-modal prototypes to allow more comprehensive support for open-world semantic segmentation, and build a novel prototype-based segmentation framework to realize this promise. To be specific, unlike the straightforward combination of bi-modal clues, we decompose the high-level language information as multi-aspect prototypes and aggregate the low-level visual information as more semantic prototypes, on basis of which, a fine-grained complementary fusion makes the multi-modal prototypes more powerful and accurate to promote the prediction. Based on an elastic mask prediction module that permits any number and form of prototype inputs, we are able to solve the zero-shot, few-shot and generalized counterpart tasks in one architecture. Extensive experiments on both PASCAL-$5^i$ and COCO-$20^i$ datasets show the consistent superiority of the proposed method compared with the previous state-of-the-art approaches, and a range of ablation studies thoroughly dissects each component in our framework both quantitatively and qualitatively that verify their effectiveness.