In this paper, we study the influence of both long and short skip connections on Fully
Convolutional Networks (FCN) for biomedical image segmentation. In standard FCNs, only …

In this paper, we propose a novel Pattern-Affinitive Propagation (PAP) framework to jointly
predict depth, surface normal and semantic segmentation. The motivation behind it comes …

Recent advances in deep learning have shown excellent performance in various scene
understanding tasks. However, in some complex environments or under challenging …

Dropout is used as a practical tool to obtain uncertainty estimates in large vision models and
reinforcement learning (RL) tasks. But to obtain well-calibrated uncertainty estimates, a grid …

Embedding methods have achieved success in face recognition by comparing facial
features in a latent semantic space. However, in a fully unconstrained face setting, the facial …

While deep neural networks have become the go-to approach in computer vision, the vast
majority of these models fail to properly capture the uncertainty inherent in their predictions …

We present a novel and practical deep fully convolutional neural network architecture for
semantic pixel-wise segmentation termed SegNet. This core trainable segmentation engine …

In this paper we address the problem of semantic labeling of indoor scenes on RGB-D data.
With the availability of RGB-D cameras, it is expected that additional depth measurement will …

Scene segmentation is a challenging task as it need label every pixel in the image. It is
crucial to exploit discriminative context and aggregate multi-scale features to achieve better …

In this paper, we argue about the importance of considering task interactions at multiple
scales when distilling task information in a multi-task learning setup. In contrast to common …