  Transformers have become prevalent in computer vision due to their
performance and flexibility in modelling complex operations. Of particular
significance is the 'cross-attention' operation, which allows a vector
representation (e.g. of an object in an image) to be learned by attending to an
arbitrarily sized set of input features. Recently, "Masked Attention" was
proposed in which a given object representation only attends to those image
pixel features for which the segmentation mask of that object is active. This
specialization of attention proved beneficial for various image and video
segmentation tasks. In this paper, we propose another specialization of
attention which enables attending over `soft-masks' (those with continuous mask
probabilities instead of binary values), and is also differentiable through
these mask probabilities, thus allowing the mask used for attention to be
learned within the network without requiring direct loss supervision. This can
be useful for several applications. Specifically, we employ our "Differentiable
Soft-Masked Attention" for the task of Weakly-Supervised Video Object
Segmentation (VOS), where we develop a transformer-based network for VOS which
only requires a single annotated image frame for training, but can also benefit
from cycle consistency training on a video with just one annotated frame.
Although there is no loss for masks in unlabeled frames, the network is still
able to segment objects in those frames due to our novel attention formulation.
Code:
https://github.com/Ali2500/HODOR/blob/main/hodor/modelling/encoder/soft_masked_attention.py
