  Detecting transparent objects in natural scenes is challenging due to the low
contrast in texture, brightness and colors. Recent deep-learning-based works
reveal that it is effective to leverage boundaries for transparent object
detection (TOD). However, these methods usually encounter boundary-related
imbalance problem, leading to limited generation capability. Detailly, a kind
of boundaries in the background, which share the same characteristics with
boundaries of transparent objects but have much smaller amounts, usually hurt
the performance. To conquer the boundary-related imbalance problem, we propose
a novel content-dependent data augmentation method termed FakeMix. Considering
collecting these trouble-maker boundaries in the background is hard without
corresponding annotations, we elaborately generate them by appending the
boundaries of transparent objects from other samples into the current image
during training, which adjusts the data space and improves the generalization
of the models. Further, we present AdaptiveASPP, an enhanced version of ASPP,
that can capture multi-scale and cross-modality features dynamically. Extensive
experiments demonstrate that our methods clearly outperform the
state-of-the-art methods. We also show that our approach can also transfer well
on related tasks, in which the model meets similar troubles, such as mirror
detection, glass detection, and camouflaged object detection. Code will be made
publicly available.
