  This work presents a new fine-grained transparent object segmentation
dataset, termed Trans10K-v2, extending Trans10K-v1, the first large-scale
transparent object segmentation dataset. Unlike Trans10K-v1 that only has two
limited categories, our new dataset has several appealing benefits. (1) It has
11 fine-grained categories of transparent objects, commonly occurring in the
human domestic environment, making it more practical for real-world
application. (2) Trans10K-v2 brings more challenges for the current advanced
segmentation methods than its former version. Furthermore, a novel
transformer-based segmentation pipeline termed Trans2Seg is proposed. Firstly,
the transformer encoder of Trans2Seg provides the global receptive field in
contrast to CNN's local receptive field, which shows excellent advantages over
pure CNN architectures. Secondly, by formulating semantic segmentation as a
problem of dictionary look-up, we design a set of learnable prototypes as the
query of Trans2Seg's transformer decoder, where each prototype learns the
statistics of one category in the whole dataset. We benchmark more than 20
recent semantic segmentation methods, demonstrating that Trans2Seg
significantly outperforms all the CNN-based methods, showing the proposed
algorithm's potential ability to solve transparent object segmentation.
