  Recent success of pre-trained foundation vision-language models makes
Open-Vocabulary Segmentation (OVS) possible. Despite the promising performance,
this approach introduces heavy computational overheads for two challenges: 1)
large model sizes of the backbone; 2) expensive costs during the fine-tuning.
These challenges hinder this OVS strategy from being widely applicable and
affordable in real-world scenarios. Although traditional methods such as model
compression and efficient fine-tuning can address these challenges, they often
rely on heuristics. This means that their solutions cannot be easily
transferred and necessitate re-training on different models, which comes at a
cost. In the context of efficient OVS, we target achieving performance that is
comparable to or even better than prior OVS works based on large
vision-language foundation models, by utilizing smaller models that incur lower
training costs. The core strategy is to make our efficiency principled and thus
seamlessly transferable from one OVS framework to others without further
customization. Comprehensive experiments on diverse OVS benchmarks demonstrate
our superior trade-off between segmentation accuracy and computation costs over
previous works. Our code is available on https://github.com/Xujxyang/OpenTrans
