  For state-of-the-art semantic segmentation task, training convolutional
neural networks (CNNs) requires dense pixelwise ground truth (GT) labeling,
which is expensive and involves extensive human effort. In this work, we study
the possibility of using auxiliary ground truth, so-called \textit{pseudo
ground truth} (PGT) to improve the performance. The PGT is obtained by
propagating the labels of a GT frame to its subsequent frames in the video
using a simple CRF-based, cue integration framework. Our main contribution is
to demonstrate the use of noisy PGT along with GT to improve the performance of
a CNN. We perform a systematic analysis to find the right kind of PGT that
needs to be added along with the GT for training a CNN. In this regard, we
explore three aspects of PGT which influence the learning of a CNN: i) the PGT
labeling has to be of good quality; ii) the PGT images have to be different
compared to the GT images; iii) the PGT has to be trusted differently than GT.
We conclude that PGT which is diverse from GT images and has good quality of
labeling can indeed help improve the performance of a CNN. Also, when PGT is
multiple folds larger than GT, weighing down the trust on PGT helps in
improving the accuracy. Finally, We show that using PGT along with GT, the
performance of Fully Convolutional Network (FCN) on Camvid data is increased by
$2.7\%$ on IoU accuracy. We believe such an approach can be used to train CNNs
for semantic video segmentation where sequentially labeled image frames are
needed. To this end, we provide recommendations for using PGT strategically for
semantic segmentation and hence bypass the need for extensive human efforts in
labeling.
