24 citations · 106 across the 26 of their papers we have counts for
27 papers
Semantics-Depth-Symbiosis: Deeply Coupled Semi-Supervised Learning of Semantics and Depth
Nitin Bansal, Pan Ji, Junsong Yuan +1
Multi-task learning (MTL) paradigm focuses on jointly learning two or more tasks, aiming for significant improvement w.r.t model's generalizability, performance, and training/infer…
Strengthening Skeletal Action Recognizers via Leveraging Temporal Patterns
Zhenyue Qin, Pan Ji, Dongwoo Kim +3
Skeleton sequences are compact and lightweight. Numerous skeleton-based action recognizers have been proposed to classify human behaviors. In this work, we aim to incorporate compo…
Representation-Centric Survey of Supervised Skeletal Action Recognition and the New Benchmark
Yang Liu, Jiyao Yang, Madhawa Perera +8
3D skeletal action recognition has emerged as a powerful alternative to traditional RGB and depth-based approaches, offering robustness to environmental variations, computational e…
Deformable VisTR: Spatio temporal deformable attention for video instance segmentation
Sudhir Yarram, Jialian Wu, Pan Ji +2
Video instance segmentation (VIS) task requires classifying, segmenting, and tracking object instances over all frames in a video clip. Recently, VisTR has been proposed as end-to-…
MonoIndoor: Towards Good Practice of Self-Supervised Monocular Depth Estimation for Indoor Environments
Pan Ji, Runze Li, Bir Bhanu +1
Self-supervised depth estimation for indoor environments is more challenging than its outdoor counterpart in at least the following two aspects: (i) the depth range of indoor seque…
Disentangling Noise from Images: A Flow-Based Image Denoising Neural Network
Yang Liu, Saeed Anwar, Zhenyue Qin +3
The prevalent convolutional neural network (CNN) based image denoising methods extract features of images to restore the clean ground truth, achieving high denoising accuracy. Howe…
Position-Sensing Graph Neural Networks: Proactively Learning Nodes Relative Positions
Zhenyue Qin, Yiqun Zhang Saeed Anwar, Dongwoo Kim +3
Most existing graph neural networks (GNNs) learn node embeddings using the framework of message passing and aggregation. Such GNNs are incapable of learning relative positions betw…
Fusing Higher-order Features in Graph Neural Networks for Skeleton-based Action Recognition
Zhenyue Qin, Yang Liu, Pan Ji +5
Skeleton sequences are lightweight and compact, and thus are ideal candidates for action recognition on edge devices. Recent skeleton-based action recognition methods extract featu…
Invertible Denoising Network: A Light Solution for Real Noise Removal
Yang Liu, Zhenyue Qin, Saeed Anwar +4
Invertible networks have various benefits for image denoising since they are lightweight, information-lossless, and memory-saving during back-propagation. However, applying inverti…
Learning Transferable Kinematic Dictionary for 3D Human Pose and Shape Reconstruction
Ze Ma, Yifan Yao, Pan Ji +1
Estimating 3D human pose and shape from a single image is highly under-constrained. To address this ambiguity, we propose a novel prior, namely kinematic dictionary, which explicit…
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…
Degeneracy in Self-Calibration Revisited and a Deep Learning Solution for Uncalibrated SLAM
Bingbing Zhuang, Quoc-Huy Tran, Pan Ji +3
Self-calibration of camera intrinsics and radial distortion has a long history of research in the computer vision community. However, it remains rare to see real applications of su…
Neural Collaborative Subspace Clustering
Tong Zhang, Pan Ji, Mehrtash Harandi +2
We introduce the Neural Collaborative Subspace Clustering, a neural model that discovers clusters of data points drawn from a union of low-dimensional subspaces. In contrast to pre…
Noise-Aware Unsupervised Deep Lidar-Stereo Fusion
Xuelian Cheng, Yiran Zhong, Yuchao Dai +2
In this paper, we present LidarStereoNet, the first unsupervised Lidar-stereo fusion network, which can be trained in an end-to-end manner without the need of ground truth depth ma…
Unsupervised Deep Epipolar Flow for Stationary or Dynamic Scenes
Yiran Zhong, Pan Ji, Jianyuan Wang +2
Unsupervised deep learning for optical flow computation has achieved promising results. Most existing deep-net based methods rely on image brightness consistency and local smoothne…
Scalable Deep -Subspace Clustering
Tong Zhang, Pan Ji, Mehrtash Harandi +2
Subspace clustering algorithms are notorious for their scalability issues because building and processing large affinity matrices are demanding. In this paper, we introduce a metho…
Deep Subspace Clustering Networks
Pan Ji, Tong Zhang, Hongdong Li +2
We present a novel deep neural network architecture for unsupervised subspace clustering. This architecture is built upon deep auto-encoders, which non-linearly map the input data…
"Maximizing rigidity" revisited: a convex programming approach for generic 3D shape reconstruction from multiple perspective views
Pan Ji, Hongdong Li, Yuchao Dai +1
Rigid structure-from-motion (RSfM) and non-rigid structure-from-motion (NRSfM) have long been treated in the literature as separate (different) problems. Inspired by a previous wor…