Search Results for author:
Found 18 papers, 4 papers with code
Action Segmentation Using 2D Skeleton Heatmaps
This paper presents a 2D skeleton-based action segmentation method with applications in fine-grained human activity recognition.
Learning by Aligning 2D Skeleton Sequences in Time
This paper presents a self-supervised temporal video alignment framework which is useful for several fine-grained human activity understanding applications.
Permutation-Aware Action Segmentation via Unsupervised Frame-to-Segment Alignment
The frame-level prediction module is trained in an unsupervised manner via temporal optimal transport.
Inductive and Transductive Few-Shot Video Classification via Appearance and Temporal Alignments
To the best of our knowledge, our work is the first to explore transductive few-shot video classification.
Timestamp-Supervised Action Segmentation with Graph Convolutional Networks
We introduce a novel approach for temporal activity segmentation with timestamp supervision.
POODLE: Improving Few-shot Learning via Penalizing Out-of-Distribution Samples
In this work, we propose to use out-of-distribution samples, i. e., unlabeled samples coming from outside the target classes, to improve few-shot learning.
POODLE: Improving Few-shot Learning via Penalizing Out-of-Distribution Samples NeurIPS 2021
In this work, we propose to use out-of-distribution samples, i. e., unlabeled samples coming from outside the target classes, to improve few-shot learning.
Unsupervised Action Segmentation by Joint Representation Learning and Online Clustering
The temporal optimal transport module enables our approach to learn effective representations for unsupervised activity segmentation.
Learning by Aligning Videos in Time
To overcome this problem, we propose a temporal regularization term (i. e., Contrastive-IDM) which encourages different frames to be mapped to different points in the embedding space.
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 stitching and rectification at one stroke.
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.
Pseudo RGB-D for Self-Improving Monocular SLAM and Depth Prediction
Classical monocular Simultaneous Localization And Mapping (SLAM) and the recently emerging convolutional neural networks (CNNs) for monocular depth prediction represent two largely disjoint approaches towards building a 3D map of the surrounding environment.
Towards Anomaly Detection in Dashcam Videos
We apply: (i) one-class classification loss and (ii) reconstruction-based loss, for anomaly detection on RetroTrucks as well as on existing static-camera datasets.
Degeneracy in Self-Calibration Revisited and a Deep Learning Solution for Uncalibrated SLAM
Self-calibration of camera intrinsics and radial distortion has a long history of research in the computer vision community.
Hierarchical Metric Learning and Matching for 2D and 3D Geometric Correspondences
Further, we propose to use activation maps at different layers of a CNN, as an effective and principled replacement for the multi-resolution image pyramids often used for matching tasks.
Deep Supervision with Intermediate Concepts
In this work, we explore an approach for injecting prior domain structure into neural network training by supervising hidden layers of a CNN with intermediate concepts that normally are not observed in practice.
Deep Supervision with Shape Concepts for Occlusion-Aware 3D Object Parsing
Monocular 3D object parsing is highly desirable in various scenarios including occlusion reasoning and holistic scene interpretation.
A Continuous Occlusion Model for Road Scene Understanding
We present a physically interpretable, continuous 3D model for handling occlusions with applications to road scene understanding.
