2 code implementations • 10 Mar 2024 • Thang Doan, Sima Behpour, Xin Li, Wenbin He, Liang Gou, Liu Ren
Few-shot Class-Incremental Learning (FSCIL) poses the challenge of retaining prior knowledge while learning from limited new data streams, all without overfitting.
2 code implementations • 25 Jun 2023 • Thang Doan, Xin Li, Sima Behpour, Wenbin He, Liang Gou, Liu Ren
We argue that this contextual information should already be embedded within the known classes.
no code implementations • 17 Nov 2021 • Hmrishav Bandyopadhyay, Zihao Deng, Leiting Ding, Sinuo Liu, Mostofa Rafid Uddin, Xiangrui Zeng, Sima Behpour, Min Xu
Cryo-Electron Tomography (cryo-ET) is a 3D imaging technology that enables the visualization of subcellular structures in situ at near-atomic resolution.
no code implementations • ICCV 2021 • Xiaoyu Zhu, Jeffrey Chen, Xiangrui Zeng, Junwei Liang, Chengqi Li, Sinuo Liu, Sima Behpour, Min Xu
We propose a novel weakly supervised approach for 3D semantic segmentation on volumetric images.
no code implementations • ICML Workshop LifelongML 2020 • Seungwon Lee, Sima Behpour, Eric Eaton
In deep networks, transferring the appropriate granularity of knowledge is as important as the transfer mechanism, and must be driven by the relationships among tasks.
no code implementations • 29 Dec 2019 • Sima Behpour
We evaluate this approach algorithmically in an important structured prediction problems: object tracking in videos.
2 code implementations • 18 Dec 2018 • Rizal Fathony, Kaiser Asif, Anqi Liu, Mohammad Ali Bashiri, Wei Xing, Sima Behpour, Xinhua Zhang, Brian D. Ziebart
We propose a robust adversarial prediction framework for general multiclass classification.
no code implementations • ICML 2018 • Rizal Fathony, Sima Behpour, Xinhua Zhang, Brian Ziebart
Many important structured prediction problems, including learning to rank items, correspondence-based natural language processing, and multi-object tracking, can be formulated as weighted bipartite matching optimizations.
no code implementations • 21 Oct 2017 • Sima Behpour, Kris M. Kitani, Brian D. Ziebart
We aim to find an optimal adversarial perturbations of the ground truth data (i. e., the worst case perturbations) that forces the object bounding box predictor to learn from the hardest distribution of perturbed examples for better test-time performance.