Open-vocabulary segmentation is a challenging task requiring segmenting and
recognizing objects from an open set of categories. One way to address this
challenge is to leverage multi-modal models, such as CLIP, to provide image and
text features in a shared embedding space, which bridges the gap between
closed-vocabulary and open-vocabulary recognition. Hence, existing methods
often adopt a two-stage framework to tackle the problem, where the inputs first
go through a mask generator and then through the CLIP model along with the
predicted masks. This process involves extracting features from images multiple
times, which can be ineffective and inefficient. By contrast, we propose to
build everything into a single-stage framework using a shared Frozen
Convolutional CLIP backbone, which not only significantly simplifies the
current two-stage pipeline, but also remarkably yields a better accuracy-cost
trade-off. The proposed FC-CLIP, benefits from the following observations: the
frozen CLIP backbone maintains the ability of open-vocabulary classification
and can also serve as a strong mask generator, and the convolutional CLIP
generalizes well to a larger input resolution than the one used during
contrastive image-text pretraining. When training on COCO panoptic data only
and testing in a zero-shot manner, FC-CLIP achieve 26.8 PQ, 16.8 AP, and 34.1
mIoU on ADE20K, 18.2 PQ, 27.9 mIoU on Mapillary Vistas, 44.0 PQ, 26.8 AP, 56.2
mIoU on Cityscapes, outperforming the prior art by +4.2 PQ, +2.4 AP, +4.2 mIoU
on ADE20K, +4.0 PQ on Mapillary Vistas and +20.1 PQ on Cityscapes,
respectively. Additionally, the training and testing time of FC-CLIP is 7.5x
and 6.6x significantly faster than the same prior art, while using 5.9x fewer
parameters. FC-CLIP also sets a new state-of-the-art performance across various
open-vocabulary semantic segmentation datasets. Code at
this https URL

使用共享的冻结卷积 CLIP 骨干网络，将当前的两阶段流程大大简化，并显著提高准确性和成本平衡，成为各种开放词汇语义分割数据集中的最新最佳表现。