SA$^2$VP: Spatially Aligned-and-Adapted Visual Prompt
Wenjie Pei Tongqi Xia Fanglin Chen Jinsong Li Jiandong Tian Guangming Lu
As a prominent parameter-efficient fine-tuning technique in NLP, prompt
tuning is being explored its potential in computer vision. Typical methods for
visual prompt tuning follow the sequential modeling paradigm stemming from NLP,
which represents an input image as a flattened sequence of token embeddings and
then learns a set of unordered parameterized tokens prefixed to the sequence
representation as the visual prompts for task adaptation of large vision
models. While such sequential modeling paradigm of visual prompt has shown
great promise, there are two potential limitations. First, the learned visual
prompts cannot model the underlying spatial relations in the input image, which
is crucial for image encoding. Second, since all prompt tokens play the same
role of prompting for all image tokens without distinction, it lacks the
fine-grained prompting capability, i.e., individual prompting for different
image tokens. In this work, we propose the \mymodel model (\emph{SA$^2$VP}),
which learns a two-dimensional prompt token map with equal (or scaled) size to
the image token map, thereby being able to spatially align with the image map.
Each prompt token is designated to prompt knowledge only for the spatially
corresponding image tokens. As a result, our model can conduct individual
prompting for different image tokens in a fine-grained manner. Moreover,
benefiting from the capability of preserving the spatial structure by the
learned prompt token map, our \emph{SA$^2$VP} is able to model the spatial
relations in the input image, leading to more effective prompting. Extensive
experiments on three challenging benchmarks for image classification
demonstrate the superiority of our model over other state-of-the-art methods
for visual prompt tuning. Code is available at
\emph{https://github.com/tommy-xq/SA2VP}.