Search Results for author:
Found 7 papers, 1 papers with code
Addressing Source Scale Bias via Image Warping for Domain Adaptation
Discovering that shifting the source scale distribution improves backbone features, we developed a instance-level warping guidance aimed at object region sampling to mitigate source scale bias in domain adaptation.
Towards Real-Time Analysis of Broadcast Badminton Videos
In this work, we propose an end-to-end framework for player movement analysis for badminton matches on live broadcast match videos.
Enhancing Visual Domain Adaptation with Source Preparation
Our Almost Unsupervised Domain Adaptation (AUDA) framework, a label-efficient semi-supervised approach for robotic scenarios -- employs Source Preparation (SP), Unsupervised Domain Adaptation (UDA) and Supervised Alignment (SA) from limited labeled data.
Learned Two-Plane Perspective Prior based Image Resampling for Efficient Object Detection
For autonomous navigation, using the same detector and scale, our approach improves detection rate by +4. 1 $AP_{S}$ or +39% and in real-time performance by +5. 3 $sAP_{S}$ or +63% for small objects over state-of-the-art (SOTA).
Streaming Video Analytics On The Edge With Asynchronous Cloud Support
In this work, we develop REACT, a framework that leverages cloud resources to execute large DNN models with higher accuracy to improve the accuracy of models running on edge devices.
SmartTennisTV: Automatic indexing of tennis videos
In this paper, we demonstrate a score based indexing approach for tennis videos.
Towards Structured Analysis of Broadcast Badminton Videos
Sports video data is recorded for nearly every major tournament but remains archived and inaccessible to large scale data mining and analytics.
