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

• URL: http://arxiv.org/abs/2408.14776v2
• Date: Wed, 27 Nov 2024 15:26:41 GMT
• Title: MROVSeg: Breaking the Resolution Curse of Vision-Language Models in Open-Vocabulary Image Segmentation
• Authors: Yuanbing Zhu, Bingke Zhu, Yingying Chen, Yunfang Niu, Ming Tang, Jinqiao Wang,
• Abstract summary: MROVSeg is a multi-resolution training framework for open-vocabulary image segmentation with a single pretrained CLIP backbone. It uses sliding windows to slice the high-resolution input into uniform patches, each matching the input size of the well-trained image encoder.
• Score: 26.667974865352708
• License:
• 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.

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