MROVSeg: Breaking the Resolution Curse of Vision-Language Models in Open-Vocabulary Image Segmentation

TL;DR

MROVSeg introduces a multi-resolution training framework for open-vocabulary image segmentation using a single CLIP backbone, effectively capturing fine details without high computational costs, and achieves state-of-the-art results.

Contribution

It proposes a novel multi-resolution training method with a Multi-Res Adapter and Masked Attention to improve segmentation detail and accuracy using only one pretrained CLIP model.

Findings

Outperforms existing methods on standard benchmarks.

Effectively captures fine details in high-resolution images.

Reduces computational overhead compared to multi-backbone approaches.

Abstract

Pretrained vision-language models (VLMs), \eg CLIP, are increasingly used to bridge the gap between open- and close-vocabulary recognition in open-vocabulary image segmentation. As VLMs are generally pretrained with low-resolution images (e.g. $224\times224$), most previous methods operate only on downscaled images. We question this design as low resolution features often fail to preserve fine details. A typical solution is to employ additional image backbones for high-resolution inputs, but it also introduce significant computation overhead. Therefore, we propose MROVSeg, a multi-resolution training framework for open-vocabulary image segmentation with a single pretrained CLIP backbone, that uses sliding windows to slice the high-resolution input into uniform patches, each matching the input size of the well-trained image encoder. Its key components include a Multi-Res Adapter, which restores the spatial geometry and grasps local-global correspondences across patches by interacting with multi-resolution features. To achieve accurate segmentation, we introduce Multi-grained Masked Attention scheme to aggregate multi-grained semantics from multi-resolution CLIP features to object queries. Through comprehensive experiments, we demonstrate the superiority of MROVSeg on well-established open-vocabulary image segmentation benchmarks, establishing new standards for open-vocabulary image segmentation.