no code implementations • ECCV 2020 • Zudi Lin, Donglai Wei, Won-Dong Jang, Siyan Zhou, Xupeng Chen, Xueying Wang, Richard Schalek, Daniel Berger, Brian Matejek, Lee Kamentsky, Adi Peleg, Daniel Haehn, Thouis Jones, Toufiq Parag, Jeff Lichtman, Hanspeter Pfister
As a use case, we build an end-to-end active learning framework with our query suggestion method for 3D synapse detection and mitochondria segmentation in connectomics.
no code implementations • CVPR 2022 • Donglai Wei, Siddhant Kharbanda, Sarthak Arora, Roshan Roy, Nishant Jain, Akash Palrecha, Tanav Shah, Shray Mathur, Ritik Mathur, Abhijay Kemkar, Anirudh Chakravarthy, Zudi Lin, Won-Dong Jang, Yansong Tang, Song Bai, James Tompkin, Philip H.S. Torr, Hanspeter Pfister
Many video understanding tasks require analyzing multi-shot videos, but existing datasets for video object segmentation (VOS) only consider single-shot videos.
1 code implementation • 15 Nov 2021 • Anirudh S Chakravarthy, Won-Dong Jang, Zudi Lin, Donglai Wei, Song Bai, Hanspeter Pfister
Motivated by this, we propose a video instance segmentation method that alleviates the problem due to missing detections.
Ranked #40 on Video Instance Segmentation on YouTube-VIS validation
no code implementations • 13 Jul 2021 • Stanislav Lukyanenko, Won-Dong Jang, Donglai Wei, Robbert Struyven, Yoon Kim, Brian Leahy, Helen Yang, Alexander Rush, Dalit Ben-Yosef, Daniel Needleman, Hanspeter Pfister
In this work, we propose a two-stream model for developmental stage classification.
no code implementations • ICCV 2021 • Salma Abdel Magid, Yulun Zhang, Donglai Wei, Won-Dong Jang, Zudi Lin, Yun Fu, Hanspeter Pfister
Specifically, we propose a dynamic high-pass filtering (HPF) module that locally applies adaptive filter weights for each spatial location and channel group to preserve high-frequency signals.
no code implementations • 2 Dec 2020 • Won-Dong Jang, Donglai Wei, Xingxuan Zhang, Brian Leahy, Helen Yang, James Tompkin, Dalit Ben-Yosef, Daniel Needleman, Hanspeter Pfister
To alleviate the problem, we propose to classify input features into intermediate shape codes and recover complete object shapes from them.
1 code implementation • Medical Image Computing and Computer Assisted Intervention 2020 • Donglai Wei, Zudi Lin, Daniel Franco-Barranco, Nils Wendt, Xingyu Liu, Wenjie Yin, Xin Huang, Aarush Gupta, Won-Dong Jang, Xueying Wang, Ignacio Arganda-Carreras, Jeff Lichtman, Hanspeter Pfister
On MitoEM, we find existing instance segmentation methods often fail to correctly segment mitochondria with complex shapes or close contacts with other instances.
Ranked #2 on 3D Instance Segmentation on MitoEM (AP75-R-Test metric)
no code implementations • 29 May 2020 • Brian D. Leahy, Won-Dong Jang, Helen Y. Yang, Robbert Struyven, Donglai Wei, Zhe Sun, Kylie R. Lee, Charlotte Royston, Liz Cam, Yael Kalma, Foad Azem, Dalit Ben-Yosef, Hanspeter Pfister, Daniel Needleman
A major challenge in clinical In-Vitro Fertilization (IVF) is selecting the highest quality embryo to transfer to the patient in the hopes of achieving a pregnancy.
1 code implementation • 27 Sep 2019 • Abhimanyu Talwar, Zudi Lin, Donglai Wei, Yuesong Wu, Bowen Zheng, Jinglin Zhao, Won-Dong Jang, Xueying Wang, Jeff W. Lichtman, Hanspeter Pfister
Next, we develop nomenclature rules for pyramidal neurons and mitochondria from the reduced graph and finally learn the feature embedding for shape manipulation.
no code implementations • CVPR 2019 • Won-Dong Jang, Chang-Su Kim
An interactive image segmentation algorithm, which accepts user-annotations about a target object and the background, is proposed in this work.
Ranked #9 on Interactive Segmentation on SBD
no code implementations • ICCV 2017 • Se-Ho Lee, Won-Dong Jang, Chang-Su Kim
A temporal superpixel algorithm based on proximity-weighted patch matching (TS-PPM) is proposed in this work.
no code implementations • CVPR 2017 • Se-Ho Lee, Won-Dong Jang, Chang-Su Kim
We initialize superpixel labels in each frame by transferring those in the previous frame and refine the labels to make superpixels temporally consistent as well as compatible with object contours.
no code implementations • CVPR 2017 • Won-Dong Jang, Chang-Su Kim
A semi-supervised online video object segmentation algorithm, which accepts user annotations about a target object at the first frame, is proposed in this work.
Ranked #69 on Semi-Supervised Video Object Segmentation on DAVIS 2016
no code implementations • CVPR 2016 • Won-Dong Jang, Chulwoo Lee, Chang-Su Kim
Then, we minimize a hybrid of the three energies to separate a primary object from its background.
no code implementations • CVPR 2016 • Yeong Jun Koh, Won-Dong Jang, Chang-Su Kim
By superposing the foreground and background features, we build the object recurrence model, the background model, and the primary object model.
no code implementations • CVPR 2015 • Chulwoo Lee, Won-Dong Jang, Jae-Young Sim, Chang-Su Kim
A graph-based system to simulate the movements and interactions of multiple random walkers (MRW) is proposed in this work.