1 research outputs found
Enhancing Salient Object Segmentation Through Attention
Segmenting salient objects in an image is an important vision task with
ubiquitous applications. The problem becomes more challenging in the presence
of a cluttered and textured background, low resolution and/or low contrast
images. Even though existing algorithms perform well in segmenting most of the
object(s) of interest, they often end up segmenting false positives due to
resembling salient objects in the background. In this work, we tackle this
problem by iteratively attending to image patches in a recurrent fashion and
subsequently enhancing the predicted segmentation mask. Saliency features are
estimated independently for every image patch, which are further combined using
an aggregation strategy based on a Convolutional Gated Recurrent Unit (ConvGRU)
network. The proposed approach works in an end-to-end manner, removing
background noise and false positives incrementally. Through extensive
evaluation on various benchmark datasets, we show superior performance to the
existing approaches without any post-processing.Comment: CVPRW - Deep Vision 201