Human pose estimation refers to the estimation of the location of body parts and how they
are connected in an image. Human pose estimation from monocular images has wide …

Vision-based monocular human pose estimation, as one of the most fundamental and
challenging problems in computer vision, aims to obtain posture of the human body from …

We propose a novel multi-task learning architecture, which allows learning of task-specific
feature-level attention. Our design, the Multi-Task Attention Network (MTAN), consists of a …

Multi-task learning in Convolutional Networks has displayed remarkable success in the field
of recognition. This success can be largely attributed to learning shared representations …

We present an algorithm for simultaneous face detection, landmarks localization, pose
estimation and gender recognition using deep convolutional neural networks (CNN). The …

Object detection performance, as measured on the canonical PASCAL VOC Challenge
datasets, plateaued in the final years of the competition. The best-performing methods were …

Recognition algorithms based on convolutional networks (CNNs) typically use the output of
the last layer as feature representation. However, the information in this layer may be too …

Current state-of-the-art semantic segmentation methods often apply high-resolution input to
attain high performance, which brings large computation budgets and limits their …

We characterize the problem of pose estimation for rigid objects in terms of determining
viewpoint to explain coarse pose and keypoint prediction to capture the finer details. We …

Existing image-based activity understanding methods mainly adopt direct map**, ie from
image to activity concepts, which may encounter performance bottleneck since the huge …