  Video Salient Document Detection (VSDD) is an essential task of practical
computer vision, which aims to highlight visually salient document regions in
video frames. Previous techniques for VSDD focus on learning features without
considering the cooperation among and across the appearance and motion cues and
thus fail to perform in practical scenarios. Moreover, most of the previous
techniques demand high computational resources, which limits the usage of such
systems in resource-constrained settings. To handle these issues, we propose
VS-Net, which captures multi-scale spatiotemporal information with the help of
dilated depth-wise separable convolution and Approximation Rank Pooling. VS-Net
extracts the key features locally from each frame across embedding sub-spaces
and forwards the features between adjacent and parallel nodes, enhancing model
performance globally. Our model generates saliency maps considering both the
background and foreground simultaneously, making it perform better in
challenging scenarios. The immense experiments regulated on the benchmark
MIDV-500 dataset show that the VS-Net model outperforms state-of-the-art
approaches in both time and robustness measures.
