Biomedical imaging is a driver of scientific discovery and a core component of medical care
and is being stimulated by the field of deep learning. While semantic segmentation …

In this work, we introduce Panoptic-DeepLab, a simple, strong, and fast system for panoptic
segmentation, aiming to establish a solid baseline for bottom-up methods that can achieve …

Background: Over half a million individuals are diagnosed with head and neck cancer each
year globally. Radiotherapy is an important curative treatment for this disease, but it requires …

Cascade is a classic yet powerful architecture that has boosted performance on various
tasks. However, how to introduce cascade to instance segmentation remains an open …

Resembling the rapid learning capability of human, low-shot learning empowers vision
systems to understand new concepts by training with few samples. Leading approaches …

Recent work has made significant progress in improving spatial resolution for pixelwise
labeling with Fully Convolutional Network (FCN) framework by employing Dilated/Atrous …

Many of the recent successful methods for video object segmentation (VOS) are overly
complicated, heavily rely on fine-tuning on the first frame, and/or are slow, and are hence of …

From the autonomous car driving to medical diagnosis, the requirement of the task of image
segmentation is everywhere. Segmentation of an image is one of the indispensable tasks in …

Recent studies witnessed that context features can significantly improve the performance of
deep semantic segmentation networks. Current context based segmentation methods differ …

In this work, we revisit atrous convolution, a powerful tool to explicitly adjust filter's field-of-
view as well as control the resolution of feature responses computed by Deep Convolutional …