Employing part-level features offers fine-grained information for pedestrian image
description. A prerequisite of part discovery is that each part should be well located. Instead …

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 …

A recent approach for object detection and human pose estimation is to regress bounding
boxes or human keypoints from a central point on the object or person. While this center …

We develop a robust multi-scale structure-aware neural network for human pose estimation.
This method improves the recent deep conv-deconv hourglass models with four key …

In this paper, we propose a novel method called Residual Steps Network (RSN). RSN
aggregates features with the same spatial size (Intra-level features) efficiently to obtain …

Compositional models represent patterns with hierarchies of meaningful parts and subparts.
Their ability to characterize high-order relationships among body parts helps resolve low …

In keypoint estimation tasks such as human pose estimation, heatmap-based regression is
the dominant approach despite possessing notable drawbacks: heatmaps intrinsically suffer …

Abstract Multi-People Tracking in an open-world setting requires a special effort in precise
detection. Moreover, temporal continuity in the detection phase gains more importance …

This paper presents a novel Mutual Learning to Adapt model (MuLA) for joint human parsing
and pose estimation. It effectively exploits mutual benefits from both tasks and …

In this paper, we propose quantized densely connected U-Nets for efficient visual landmark
localization. The idea is that features of the same semantic meanings are globally reused …