24 citations · 106 across the 26 of their papers we have counts for
7 papers · 1 filter
Set Augmented Triplet Loss for Video Person Re-Identification
Pengfei Fang, Pan Ji, Lars Petersson +1
Modern video person re-identification (re-ID) machines are often trained using a metric learning approach, supervised by a triplet loss. The triplet loss used in video re-ID is usu…
Displacement-Invariant Matching Cost Learning for Accurate Optical Flow Estimation
Jianyuan Wang, Yiran Zhong, Yuchao Dai +3
Learning matching costs has been shown to be critical to the success of the state-of-the-art deep stereo matching methods, in which 3D convolutions are applied on a 4D feature volu…
Channel Recurrent Attention Networks for Video Pedestrian Retrieval
Pengfei Fang, Pan Ji, Jieming Zhou +2
Full attention, which generates an attention value per element of the input feature maps, has been successfully demonstrated to be beneficial in visual tasks. In this work, we prop…
Cross-Modality 3D Object Detection
Ming Zhu, Chao Ma, Pan Ji +1
In this paper, we focus on exploring the fusion of images and point clouds for 3D object detection in view of the complementary nature of the two modalities, i.e., images possess m…
Learning Monocular Visual Odometry via Self-Supervised Long-Term Modeling
Yuliang Zou, Pan Ji, Quoc-Huy Tran +2
Monocular visual odometry (VO) suffers severely from error accumulation during frame-to-frame pose estimation. In this paper, we present a self-supervised learning method for VO wi…
Understanding Road Layout from Videos as a Whole
Buyu Liu, Bingbing Zhuang, Samuel Schulter +2
In this paper, we address the problem of inferring the layout of complex road scenes from video sequences. To this end, we formulate it as a top-view road attributes prediction pro…
Pseudo RGB-D for Self-Improving Monocular SLAM and Depth Prediction
Lokender Tiwari, Pan Ji, Quoc-Huy Tran +3
Classical monocular Simultaneous Localization And Mapping (SLAM) and the recently emerging convolutional neural networks (CNNs) for monocular depth prediction represent two largely…