Anchor-free object detection with mask attention

Object Detection with Mask-based Feature Encoding

Region-based Convolutional Neural Networks (R-CNNs) have achieved great success in the field of object detection. The existing R-CNNs usually divide a Region-of-Interest (ROI) into grids, and then localize objects by utilizing the spatial information reflected by the relative position of each grid in the ROI. In this paper, we propose a novel featureencoding approach, where spatial information ...

Pseudo Mask Augmented Object Detection

In this work, we present a novel and effective framework to facilitate object detection with the instance-level segmentation information that is only supervised by bounding box annotation. Starting from the joint object detection and instance segmentation network, we propose to recursively estimate the pseudo ground-truth object masks from the instance-level object segmentation network training...

Attention-based Object Detection

Active Mask Hierarchies for Object Detection

This paper presents a new object representation, Active Mask Hierarchies (AMH), for object detection. In this representation, an object is described using a mixture of hierarchical trees where the nodes represent the object and its parts in pyramid form. To account for shape variations at a range of scales, a dictionary of masks with varied shape patterns are attached to the nodes at different ...

Video Object Segmentation with Joint Re-identification and Attention-Aware Mask Propagation

The problem of video object segmentation can become extremely challenging when multiple instances co-exist. While each instance may exhibit large scale and pose variations, the problem is compounded when instances occlude each other causing failures in tracking. In this study, we formulate a deep recurrent network that is capable of segmenting and tracking objects in video simultaneously by the...