1 code implementation • 23 Oct 2023 • Tai-Yu Pan, Chenyang Ma, Tianle Chen, Cheng Perng Phoo, Katie Z Luo, Yurong You, Mark Campbell, Kilian Q. Weinberger, Bharath Hariharan, Wei-Lun Chao
Accurate 3D object detection and understanding for self-driving cars heavily relies on LiDAR point clouds, necessitating large amounts of labeled data to train.
1 code implementation • CVPR 2023 • Tai-Yu Pan, Qing Liu, Wei-Lun Chao, Brian Price
Second, we introduce a novel approach to improve part segmentation on unseen objects, inspired by an interesting finding -- for unseen objects, the pixel-wise features extracted by the model often reveal high-quality part segments.
no code implementations • 22 Feb 2022 • Cheng Zhang, Tai-Yu Pan, Tianle Chen, Jike Zhong, WenJin Fu, Wei-Lun Chao
One fundamental challenge in building an instance segmentation model for a large number of classes in complex scenes is the lack of training examples, especially for rare objects.
1 code implementation • CVPR 2022 • Chan Hee Song, Jihyung Kil, Tai-Yu Pan, Brian M. Sadler, Wei-Lun Chao, Yu Su
We study the problem of developing autonomous agents that can follow human instructions to infer and perform a sequence of actions to complete the underlying task.
1 code implementation • NeurIPS 2021 • Tai-Yu Pan, Cheng Zhang, Yandong Li, Hexiang Hu, Dong Xuan, Soravit Changpinyo, Boqing Gong, Wei-Lun Chao
We propose NorCal, Normalized Calibration for long-tailed object detection and instance segmentation, a simple and straightforward recipe that reweighs the predicted scores of each class by its training sample size.
1 code implementation • ICCV 2021 • Cheng Zhang, Tai-Yu Pan, Yandong Li, Hexiang Hu, Dong Xuan, Soravit Changpinyo, Boqing Gong, Wei-Lun Chao
Many objects do not appear frequently enough in complex scenes (e. g., certain handbags in living rooms) for training an accurate object detector, but are often found frequently by themselves (e. g., in product images).