Open-vocabulary semantic segmentation requires models to effectively integrate visual representations with open-vocabulary semantic labels. While Contrastive Language-Image Pre-training (CLIP) models shine in recognizing visual concepts from text, they often struggle with segment coherence due to their limited localization ability. In contrast, Vision Foundation Models (VFMs) excel at acquiring spatially consistent local visual representations, yet they fall short in semantic understanding. This paper introduces ProxyCLIP, an innovative framework designed to harmonize the strengths of both CLIP and VFMs, facilitating enhanced open-vocabulary semantic segmentation. ProxyCLIP leverages the spatial feature correspondence from VFMs as a form of proxy attention to augment CLIP, thereby inheriting the VFMs' robust local consistency and maintaining CLIP's exceptional zero-shot transfer capacity. We propose an adaptive normalization and masking strategy to get the proxy attention from VFMs, allowing for adaptation across different VFMs. Remarkably, as a training-free approach, ProxyCLIP significantly improves the average mean Intersection over Union (mIoU) across eight benchmarks from 40.3 to 44.4, showcasing its exceptional efficacy in bridging the gap between spatial precision and semantic richness for the open-vocabulary segmentation task.

本研究解决了开放词汇语义分割中模型在视觉表示与开放词汇语义标签整合的困难。提出的ProxyCLIP框架通过利用视觉基础模型的空间特征，将其作为代理注意力增强CLIP，从而实现空间精度与语义丰富性的有效结合。研究表明，ProxyCLIP显著提高了多项基准测试的平均均值交并比，从40.3提升至44.4，展现了其在开放词汇分割任务中的卓越效果。

ProxyCLIP: 代理注意力提升CLIP在开放词汇分割中的表现