Image semantic segmentation is more and more being of interest for computer vision and
machine learning researchers. Many applications on the rise need accurate and efficient …

Object detection, including objectness detection (OD), salient object detection (SOD), and
category-specific object detection (COD), is one of the most fundamental yet challenging …

Modern CNN-based object detectors rely on bounding box regression and non-maximum
suppression to localize objects. While the probabilities for class labels naturally reflect …

The way that information propagates in neural networks is of great importance. In this paper,
we propose Path Aggregation Network (PANet) aiming at boosting information flow in …

Recent work has shown that depth estimation from a stereo pair of images can be
formulated as a supervised learning task to be resolved with convolutional neural networks …

Benefit from the quick development of deep learning techniques, salient object detection has
achieved remarkable progresses recently. However, there still exists following two major …

For object detection, the two-stage approach (eg, Faster R-CNN) has been achieving the
highest accuracy, whereas the one-stage approach (eg, SSD) has the advantage of high …

We present a box-free bottom-up approach for the tasks of pose estimation and instance
segmentation of people in multi-person images using an efficient single-shot model. The …

An analysis of different techniques for recognizing and detecting objects under extreme
scale variation is presented. Scale specific and scale invariant design of detectors are …

Abstract We introduce Similarity Group Proposal Network (SGPN), a simple and intuitive
deep learning framework for 3D object instance segmentation on point clouds. SGPN uses a …