Most contemporary robots have depth sensors, and research on semantic segmentation with RGBD images has shown that depth images boost the accuracy of segmentation. Since it is time-consuming to annotate images with semantic labels per pixel, it would be ideal if we could avoid this laborious work by utilizing an existing dataset or a synthetic dataset which we can generate on our own. Robot motions are often tested in a synthetic environment, where multichannel (eg, RGB + depth + instance boundary) images plus their pixel-level semantic labels are available. However, models trained simply on synthetic images tend to demonstrate poor performance on real images. In order to address this, we propose two approaches that can efficiently exploit multichannel inputs combined with an unsupervised domain adaptation (UDA) algorithm. One is a fusion-based approach that uses depth images as inputs. The other is a multitask learning approach that uses depth images as outputs. We demonstrated that the segmentation results were improved by using a multitask learning approach with a post-process and created a benchmark for this task.
Multi-task learning is a promising approach for efficiently and effectively addressing multiple mutually related recognition tasks. Many scene understanding tasks such as semantic segmentation and depth prediction can be framed as cross-modal encoding decoding, and hence most of the prior work used multi-modal datasets for multi-task learning. However, the inter-modal commonalities, such as one across image, depth, and semantic labels, have not been fully exploited. We propose a multi-modal encoder-decoder networks to harness the multi-modal nature of multi-task scene recognition. In addition to the shared latent representation among encoder-decoder pairs, our model also has shared skip connections from different encoders. By combining these two representation sharing mechanisms, the proposed method efficiently learns a shared feature representation among all modalities in the training data. Experiments using two public datasets shows the advantage of our method over baseline methods that are based on encoder-decoder networks and multi-modal auto-encoders. Numerous deep learning applications benefit from multitask learning with multiple regression and classification objectives. In this paper we make the observation that the performance of such systems is strongly dependent on the relative weighting between each task's loss. Tuning these weights by hand is a difficult and expensive process, making multi-task learning prohibitive in practice. We propose a principled approach to multi-task deep learning which weighs multiple loss functions by considering the homoscedastic uncertainty of each task. This allows us to simultaneously learn various quantities with different units or scales in both classification and regression settings. We demonstrate our model learning per-pixel depth regression, semantic and instance segmentation from a monocular input image. Perhaps surprisingly, we show our model can learn multi-task weightings and outperform separate models trained individually on each task.