CLIP for Lightweight Semantic Segmentation
Ke Jin Wankou Yang
The large-scale pretrained model CLIP, trained on 400 million image-text
pairs, offers a promising paradigm for tackling vision tasks, albeit at the
image level. Later works, such as DenseCLIP and LSeg, extend this paradigm to
dense prediction, including semantic segmentation, and have achieved excellent
results. However, the above methods either rely on CLIP-pretrained visual
backbones or use none-pretrained but heavy backbones such as Swin, while
falling ineffective when applied to lightweight backbones. The reason for this
is that the lightweitht networks, feature extraction ability of which are
relatively limited, meet difficulty embedding the image feature aligned with
text embeddings perfectly. In this work, we present a new feature fusion module
which tackles this problem and enables language-guided paradigm to be applied
to lightweight networks. Specifically, the module is a parallel design of CNN
and transformer with a two-way bridge in between, where CNN extracts spatial
information and visual context of the feature map from the image encoder, and
the transformer propagates text embeddings from the text encoder forward. The
core of the module is the bidirectional fusion of visual and text feature
across the bridge which prompts their proximity and alignment in embedding
space. The module is model-agnostic, which can not only make language-guided
lightweight semantic segmentation practical, but also fully exploit the
pretrained knowledge of language priors and achieve better performance than
previous SOTA work, such as DenseCLIP, whatever the vision backbone is.
Extensive experiments have been conducted to demonstrate the superiority of our
method.