In order to enhance the performance of PDLF-Net, we augment all the weakly visible EMs and their GT images. Then joint pairwise features are extracted from each image and concatenated to different blocks. Each block is trained and tested independently for each dataset class.   Comparing the observation performance from figure 11, the PDLF-Net shows better segmentation results. For instance, in **data** class DC3 and DC4 (2 nd and 3 rd rows from the top) SegNet in (c) has not been able to show the foreground while there is a good segmented output of the same image by PDLF-Net in (e) and (f). In DC8 (last row), SegNet over-segments the image while good visual results are observed by PDLF-Net when concatenation of pairwise feature is at block 2, 3 and 5. Generally, the visual results show great improvement of segmentation results when using PDLF-Net.