1 code implementation • 11 Apr 2022 • Rohan Sarkar, Navaneeth Bodla, Mariya I. Vasileva, Yen-Liang Lin, Anurag Beniwal, Alan Lu, Gerard Medioni
For compatibility prediction, we design an outfit token to capture a global outfit representation and train the framework using a classification loss.
no code implementations • CVPR 2021 • Jiali Duan, Yen-Liang Lin, Son Tran, Larry S. Davis, C. -C. Jay Kuo
We first train a teacher model on the labeled data and use it to generate pseudo labels for the unlabeled data.
1 code implementation • CVPR 2020 • Yen-Liang Lin, Son Tran, Larry S. Davis
We evaluate our method on the outfit compatibility, FITB and new retrieval tasks.
1 code implementation • 1 Aug 2019 • Hung-Yueh Chiang, Yen-Liang Lin, Yueh-Cheng Liu, Winston H. Hsu
We present a new unified point-based framework for 3D point cloud segmentation that effectively optimizes pixel-level features, geometrical structures and global context priors of an entire scene.
no code implementations • 16 Jan 2019 • Xia Li, Yen-Liang Lin, James Miller, Alex Cheon, Walt Dixon
As we begin to consider modeling large, realistic 3D building scenes, it becomes necessary to consider a more compact representation over the polygonal mesh model.
no code implementations • ECCV 2018 • Zuxuan Wu, Xintong Han, Yen-Liang Lin, Mustafa Gkhan Uzunbas, Tom Goldstein, Ser Nam Lim, Larry S. Davis
In particular, given an image from the source domain and unlabeled samples from the target domain, the generator synthesizes new images on-the-fly to resemble samples from the target domain in appearance and the segmentation network further refines high-level features before predicting semantic maps, both of which leverage feature statistics of sampled images from the target domain.
no code implementations • ICCV 2017 • Meng-Ru Hsieh, Yen-Liang Lin, Winston H. Hsu
Existing counting methods often adopt regression-based approaches and cannot precisely localize the target objects, which hinders the further analysis (e. g., high-level understanding and fine-grained classification).
Ranked #8 on Object Counting on CARPK
no code implementations • CVPR 2017 • Kuan-Lun Tseng, Yen-Liang Lin, Winston Hsu, Chung-Yang Huang
Deep learning models such as convolutional neural net- work have been widely used in 3D biomedical segmentation and achieve state-of-the-art performance.
no code implementations • CVPR 2015 • Ting-Hsuan Chao, Yen-Liang Lin, Yin-Hsi Kuo, Winston H. Hsu
Our method can reconstruct filters by minimizing score map error, while sparse coding reconstructs filters by minimizing appearance error.