Hybrid Mamba for Few-Shot Segmentation

Qianxiong Xu, Xuanyi Liu, Lanyun Zhu, Guosheng Lin, Cheng Long, Ziyue Li, Rui Zhao

Abstract:

Many few-shot segmentation (FSS) methods use cross attention to fuse support
foreground (FG) into query features, regardless of the quadratic complexity. A
recent advance Mamba can also well capture intra-sequence dependencies, yet the
complexity is only linear. Hence, we aim to devise a cross (attention-like) Mamba
to capture inter-sequence dependencies for FSS. A simple idea is to scan on support
features to selectively compress them into the hidden state, which is then used as the
initial hidden state to sequentially scan query features. Nevertheless, it suffers from
(1) support forgetting issue: query features will also gradually be compressed when
scanning on them, so the support features in hidden state keep reducing, and many
query pixels cannot fuse sufficient support features; (2) intra-class gap issue: query
FG is essentially more similar to itself rather than to support FG, i.e., query may
prefer not to fuse support features but their own ones from the hidden state, yet the
success of FSS relies on the effective use of support information. To tackle them, we
design a hybrid Mamba network (HMNet), including (1) a support recapped Mamba
to periodically recap the support features when scanning query, so the hidden state
can always contain rich support information; (2) a query intercepted Mamba to
forbid the mutual interactions among query pixels, and encourage them to fuse
more support features from the hidden state. Consequently, the support information
is better utilized, leading to better performance. Extensive experiments have been
conducted on two public benchmarks, showing the superiority of HMNet.