19 citations · 63 across the 17 of their papers we have counts for
19 papers · 1 filter
Unsupervised Skeleton-Based Action Segmentation via Hierarchical Spatiotemporal Vector Quantization
Umer Ahmed, Syed Ahmed Mahmood, Fawad Javed Fateh +3
We propose a novel hierarchical spatiotemporal vector quantization framework for unsupervised skeleton-based temporal action segmentation. We first introduce a hierarchical approac…
Procedure Learning via Regularized Gromov-Wasserstein Optimal Transport
Syed Ahmed Mahmood, Ali Shah Ali, Umer Ahmed +3
We study self-supervised procedure learning, which discovers key steps and their order from a set of unlabeled videos. Previous methods typically learn frame-to-frame correspondenc…
Joint Self-Supervised Video Alignment and Action Segmentation
Ali Shah Ali, Syed Ahmed Mahmood, Mubin Saeed +3
We introduce a novel approach for simultaneous self-supervised video alignment and action segmentation based on a unified optimal transport framework. In particular, we first tackl…
TemporalVLM: Video LLMs for Temporal Reasoning in Long Videos
Fawad Javed Fateh, Umer Ahmed, Hamza Khan +2
We introduce TemporalVLM, a video large language model (video LLM) for temporal reasoning and fine-grained understanding in long videos. Our approach includes a visual encoder for…
Action Segmentation Using 2D Skeleton Heatmaps and Multi-Modality Fusion
Syed Waleed Hyder, Muhammad Usama, Anas Zafar +5
This paper presents a 2D skeleton-based action segmentation method with applications in fine-grained human activity recognition. In contrast with state-of-the-art methods which dir…
Learning by Aligning 2D Skeleton Sequences and Multi-Modality Fusion
Quoc-Huy Tran, Muhammad Ahmed, Murad Popattia +3
This paper presents a self-supervised temporal video alignment framework which is useful for several fine-grained human activity understanding applications. In contrast with the st…
Permutation-Aware Action Segmentation via Unsupervised Frame-to-Segment Alignment
Quoc-Huy Tran, Ahmed Mehmood, Muhammad Ahmed +4
This paper presents an unsupervised transformer-based framework for temporal activity segmentation which leverages not only frame-level cues but also segment-level cues. This is in…
Timestamp-Supervised Action Segmentation with Graph Convolutional Networks
Hamza Khan, Sanjay Haresh, Awais Ahmed +4
We introduce a novel approach for temporal activity segmentation with timestamp supervision. Our main contribution is a graph convolutional network, which is learned in an end-to-e…
Inductive and Transductive Few-Shot Video Classification via Appearance and Temporal Alignments
Khoi D. Nguyen, Quoc-Huy Tran, Khoi Nguyen +2
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…
Unsupervised Action Segmentation by Joint Representation Learning and Online Clustering
Sateesh Kumar, Sanjay Haresh, Awais Ahmed +3
We present a novel approach for unsupervised activity segmentation which uses video frame clustering as a pretext task and simultaneously performs representation learning and onlin…
Learning by Aligning Videos in Time
Sanjay Haresh, Sateesh Kumar, Huseyin Coskun +4
We present a self-supervised approach for learning video representations using temporal video alignment as a pretext task, while exploiting both frame-level and video-level informa…
Image Stitching and Rectification for Hand-Held Cameras
Bingbing Zhuang, Quoc-Huy Tran
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 ca…
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…
Towards Anomaly Detection in Dashcam Videos
Sanjay Haresh, Sateesh Kumar, M. Zeeshan Zia +1
Inexpensive sensing and computation, as well as insurance innovations, have made smart dashboard cameras ubiquitous. Increasingly, simple model-driven computer vision algorithms fo…
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…
Hierarchical Metric Learning and Matching for 2D and 3D Geometric Correspondences
Mohammed E. Fathy, Quoc-Huy Tran, M. Zeeshan Zia +2
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 t…
Deep Supervision with Intermediate Concepts
Chi Li, M. Zeeshan Zia, Quoc-Huy Tran +3
Recent data-driven approaches to scene interpretation predominantly pose inference as an end-to-end black-box mapping, commonly performed by a Convolutional Neural Network (CNN). H…
Deep Supervision with Shape Concepts for Occlusion-Aware 3D Object Parsing
Chi Li, M. Zeeshan Zia, Quoc-Huy Tran +3
Monocular 3D object parsing is highly desirable in various scenarios including occlusion reasoning and holistic scene interpretation. We present a deep convolutional neural network…