227 citations · 441 across the 20 of their papers we have counts for
7 papers · 1 filter
MindGPT: Interpreting What You See with Non-invasive Brain Recordings
Jiaxuan Chen, Yu Qi, Yueming Wang +1
Decoding of seen visual contents with non-invasive brain recordings has important scientific and practical values. Efforts have been made to recover the seen images from brain sign…
SceneCalib: Automatic Targetless Calibration of Cameras and Lidars in Autonomous Driving
Ayon Sen, Gang Pan, Anton Mitrokhin +1
Accurate camera-to-lidar calibration is a requirement for sensor data fusion in many 3D perception tasks. In this paper, we present SceneCalib, a novel method for simultaneous self…
DANI-Net: Uncalibrated Photometric Stereo by Differentiable Shadow Handling, Anisotropic Reflectance Modeling, and Neural Inverse Rendering
Zongrui Li, Qian Zheng, Boxin Shi +2
Uncalibrated photometric stereo (UPS) is challenging due to the inherent ambiguity brought by the unknown light. Although the ambiguity is alleviated on non-Lambertian objects, the…
Rethinking Sampling Strategies for Unsupervised Person Re-identification
Xumeng Han, Xuehui Yu, Guorong Li +5
Unsupervised person re-identification (re-ID) remains a challenging task. While extensive research has focused on the framework design and loss function, this paper shows that samp…
Improving Object Detection with Deep Convolutional Networks via Bayesian Optimization and Structured Prediction
Yuting Zhang, Kihyuk Sohn, Ruben Villegas +2
Object detection systems based on the deep convolutional neural network (CNN) have recently made ground- breaking advances on several object detection benchmarks. While the feature…
Robust Face Recognition by Constrained Part-based Alignment
Yuting Zhang, Kui Jia, Yueming Wang +3
Developing a reliable and practical face recognition system is a long-standing goal in computer vision research. Existing literature suggests that pixel-wise face alignment is the…
Spectral Sparse Representation for Clustering: Evolved from PCA, K-means, Laplacian Eigenmap, and Ratio Cut
Zhenfang Hu, Gang Pan, Yueming Wang +1
Dimensionality reduction, cluster analysis, and sparse representation are basic components in machine learning. However, their relationships have not yet been fully investigated. I…