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Chapter and Conference Paper
Inductive and Transductive Few-Shot Video Classification via Appearance and Temporal Alignments
We present a novel method for few-shot video classification, which performs appearance and temporal alignments. In particular, given a pair of query and support videos, we conduct appearance alignment via fram...
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Chapter and Conference Paper
Learning Monocular Visual Odometry via Self-Supervised Long-Term Modeling
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 with special consideration for ...
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Chapter and Conference Paper
Image Stitching and Rectification for Hand-Held Cameras
In this paper, we derive a new differential homography that can account for the scanline-varying camera poses in Rolling Shutter (RS) cameras, and demonstrate its application to carry out RS-aware image stitch...
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Chapter and Conference Paper
Pseudo RGB-D for Self-improving Monocular SLAM and Depth Prediction
Classical monocular Simultaneous Localization And Map** (SLAM) and the recently emerging convolutional neural networks (CNNs) for monocular depth prediction represent two largely disjoint approaches towards ...
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Chapter and Conference Paper
Hierarchical Metric Learning and Matching for 2D and 3D Geometric Correspondences
Interest point descriptors have fueled progress on almost every problem in computer vision. Recent advances in deep neural networks have enabled task-specific learned descriptors that outperform hand-crafted d...
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Article
Sampling Minimal Subsets with Large Spans for Robust Estimation
When sampling minimal subsets for robust parameter estimation, it is commonly known that obtaining an all-inlier minimal subset is not sufficient; the points therein should also have a large spatial extent. Th...
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Chapter and Conference Paper
In Defence of RANSAC for Outlier Rejection in Deformable Registration
This paper concerns the robust estimation of non-rigid deformations from feature correspondences. We advance the surprising view that for many realistic physical deformations, the error of the mismatches (outl...
