no code implementations • 29 Mar 2023 • Yi-Syuan Liou, Tsung-Han Wu, Jia-Fong Yeh, Wen-Chin Chen, Winston H. Hsu
MuRAL identifies informative regions of various scales to reduce annotation costs for well-learned objects and improve training performance.
1 code implementation • 5 Oct 2022 • Cheng-Wei Lin, Tung-I Chen, Hsin-Ying Lee, Wen-Chin Chen, Winston H. Hsu
As global feature alignment requires the features to preserve the poses of input point clouds and local feature matching expects the features to be invariant to these poses, we propose an SE(3)-equivariant feature extractor to simultaneously generate two types of features.
1 code implementation • 27 Sep 2022 • Ching-Yu Tseng, Yi-Rong Chen, Hsin-Ying Lee, Tsung-Han Wu, Wen-Chin Chen, Winston H. Hsu
To achieve accurate 3D object detection at a low cost for autonomous driving, many multi-camera methods have been proposed and solved the occlusion problem of monocular approaches.
1 code implementation • 27 Sep 2022 • Chi-Ming Chung, Yang-Che Tseng, Ya-Ching Hsu, Xiang-Qian Shi, Yun-Hung Hua, Jia-Fong Yeh, Wen-Chin Chen, Yi-Ting Chen, Winston H. Hsu
A spatial AI that can perform complex tasks through visual signals and cooperate with humans is highly anticipated.
1 code implementation • WACV 2021 • Shuo-Diao Yang, Hung-Ting Su, Winston H. Hsu, Wen-Chin Chen
Instead of counting a pre-defined class, our model is able to count instances based on input reference images and reduces the huge cost of data collection, training and parameter tuning for each new object class.
1 code implementation • NAACL 2021 • Ke-Jyun Wang, Yun-Hsuan Liu, Hung-Ting Su, Jen-Wei Wang, Yu-Siang Wang, Winston H. Hsu, Wen-Chin Chen
To effectively apply robots in working environments and assist humans, it is essential to develop and evaluate how visual grounding (VG) can affect machine performance on occluded objects.
1 code implementation • 24 Feb 2021 • Tung-I Chen, Yueh-Cheng Liu, Hung-Ting Su, Yu-Cheng Chang, Yu-Hsiang Lin, Jia-Fong Yeh, Wen-Chin Chen, Winston H. Hsu
While recent progress has significantly boosted few-shot classification (FSC) performance, few-shot object detection (FSOD) remains challenging for modern learning systems.
Ranked #9 on Few-Shot Object Detection on MS-COCO (10-shot)