  Contrastive Vision-Language Pre-training(CLIP) demonstrates impressive
zero-shot capability. The key to improve the adaptation of CLIP to downstream
task with few exemplars lies in how to effectively model and transfer the
useful knowledge embedded in CLIP. Previous work mines the knowledge typically
based on the limited visual samples and close-set semantics (i.e., within
target category set of downstream task). However, the aligned CLIP image/text
encoders contain abundant relationships between visual features and almost
infinite open semantics, which may benefit the few-shot learning but remains
unexplored. In this paper, we propose to mine open semantics as anchors to
perform a relation transition from image-anchor relationship to image-target
relationship to make predictions. Specifically, we adopt a transformer module
which takes the visual feature as "Query", the text features of the anchors as
"Key" and the similarity matrix between the text features of anchor and target
classes as "Value". In this way, the output of such a transformer module
represents the relationship between the image and target categories, i.e., the
classification predictions. To avoid manually selecting the open semantics, we
make the [CLASS] token of input text embedding learnable. We conduct extensive
experiments on eleven representative classification datasets. The results show
that our method performs favorably against previous state-of-the-arts
considering few-shot classification settings.
