Target-aware Bi-Transformer for Few-shot Segmentation
Xianglin Wang Xiaoliu Luo Taiping Zhang
Traditional semantic segmentation tasks require a large number of labels and
are difficult to identify unlearned categories. Few-shot semantic segmentation
(FSS) aims to use limited labeled support images to identify the segmentation
of new classes of objects, which is very practical in the real world. Previous
researches were primarily based on prototypes or correlations. Due to colors,
textures, and styles are similar in the same image, we argue that the query
image can be regarded as its own support image. In this paper, we proposed the
Target-aware Bi-Transformer Network (TBTNet) to equivalent treat of support
images and query image. A vigorous Target-aware Transformer Layer (TTL) also be
designed to distill correlations and force the model to focus on foreground
information. It treats the hypercorrelation as a feature, resulting a
significant reduction in the number of feature channels. Benefit from this
characteristic, our model is the lightest up to now with only 0.4M learnable
parameters. Futhermore, TBTNet converges in only 10% to 25% of the training
epochs compared to traditional methods. The excellent performance on standard
FSS benchmarks of PASCAL-5i and COCO-20i proves the efficiency of our method.
Extensive ablation studies were also carried out to evaluate the effectiveness
of Bi-Transformer architecture and TTL.