no code implementations • 26 Jan 2024 • Baoyuan Wu, Hongrui Chen, Mingda Zhang, Zihao Zhu, Shaokui Wei, Danni Yuan, Mingli Zhu, Ruotong Wang, Li Liu, Chao Shen
We hope that our efforts could build a solid foundation of backdoor learning to facilitate researchers to investigate existing algorithms, develop more innovative algorithms, and explore the intrinsic mechanism of backdoor learning.
no code implementations • 13 Dec 2023 • Baoyuan Wu, Shaokui Wei, Mingli Zhu, Meixi Zheng, Zihao Zhu, Mingda Zhang, Hongrui Chen, Danni Yuan, Li Liu, Qingshan Liu
Adversarial phenomenon has been widely observed in machine learning (ML) systems, especially in those using deep neural networks, describing that ML systems may produce inconsistent and incomprehensible predictions with humans at some particular cases.
no code implementations • 1 Dec 2023 • Hammad A. Ayyubi, Tianqi Liu, Arsha Nagrani, Xudong Lin, Mingda Zhang, Anurag Arnab, Feng Han, Yukun Zhu, Jialu Liu, Shih-Fu Chang
We also release a large-scale dataset, VIEWS (VIdeo NEWS), to support research on this task.
no code implementations • 28 Sep 2023 • Zihao Zhu, Mingda Zhang, Shaokui Wei, Bingzhe Wu, Baoyuan Wu
The role of data in building AI systems has recently been emphasized by the emerging concept of data-centric AI.
1 code implementation • NeurIPS 2023 • Meng Liu, Mingda Zhang, Jialu Liu, Hanjun Dai, Ming-Hsuan Yang, Shuiwang Ji, Zheyun Feng, Boqing Gong
In this paper, we present a novel problem, namely video timeline modeling.
no code implementations • 14 Jul 2023 • Zihao Zhu, Mingda Zhang, Shaokui Wei, Li Shen, Yanbo Fan, Baoyuan Wu
To further integrate it with normal training process, we then propose a learnable poisoning sample selection strategy to learn the mask together with the model parameters through a min-max optimization. Specifically, the outer loop aims to achieve the backdoor attack goal by minimizing the loss based on the selected samples, while the inner loop selects hard poisoning samples that impede this goal by maximizing the loss.
no code implementations • 16 Apr 2023 • Hong-You Chen, Jike Zhong, Mingda Zhang, Xuhui Jia, Hang Qi, Boqing Gong, Wei-Lun Chao, Li Zhang
FedBasis learns a set of few shareable ``basis'' models, which can be linearly combined to form personalized models for clients.
no code implementations • CVPR 2023 • Hong-You Chen, Yandong Li, Yin Cui, Mingda Zhang, Wei-Lun Chao, Li Zhang
We study the problem of how to train a "personalization-friendly" model such that given only the task descriptions, the model can be adapted to different end-users' needs, e. g., for accurately classifying different subsets of objects.
1 code implementation • 25 Jun 2022 • Baoyuan Wu, Hongrui Chen, Mingda Zhang, Zihao Zhu, Shaokui Wei, Danni Yuan, Chao Shen
However, we find that the evaluations of new methods are often unthorough to verify their claims and accurate performance, mainly due to the rapid development, diverse settings, and the difficulties of implementation and reproducibility.
1 code implementation • AKBC 2021 • Meiqi Guo, Mingda Zhang, Siva Reddy, Malihe Alikhani
We introduce Abg-CoQA, a novel dataset for clarifying ambiguity in Conversational Question Answering systems.
no code implementations • 7 May 2021 • Mingda Zhang, Chun-Te Chu, Andrey Zhmoginov, Andrew Howard, Brendan Jou, Yukun Zhu, Li Zhang, Rebecca Hwa, Adriana Kovashka
With early termination, the average cost can be further reduced to 198M MAdds while maintaining accuracy of 80. 0% on ImageNet.
Ranked #659 on Image Classification on ImageNet
no code implementations • CVPR 2021 • Mingda Zhang, Tristan Maidment, Ahmad Diab, Adriana Kovashka, Rebecca Hwa
The observation that computer vision methods overfit to dataset specifics has inspired diverse attempts to make object recognition models robust to domain shifts.
1 code implementation • ICCV 2019 • Keren Ye, Mingda Zhang, Adriana Kovashka, Wei Li, Danfeng Qin, Jesse Berent
Learning to localize and name object instances is a fundamental problem in vision, but state-of-the-art approaches rely on expensive bounding box supervision.
no code implementations • 25 Nov 2018 • Keren Ye, Mingda Zhang, Wei Li, Danfeng Qin, Adriana Kovashka, Jesse Berent
To alleviate the cost of obtaining accurate bounding boxes for training today's state-of-the-art object detection models, recent weakly supervised detection work has proposed techniques to learn from image-level labels.
no code implementations • 21 Jul 2018 • Mingda Zhang, Rebecca Hwa, Adriana Kovashka
Images and text in advertisements interact in complex, non-literal ways.
no code implementations • CVPR 2017 • Zaeem Hussain, Mingda Zhang, Xiaozhong Zhang, Keren Ye, Christopher Thomas, Zuha Agha, Nathan Ong, Adriana Kovashka
There is more to images than their objective physical content: for example, advertisements are created to persuade a viewer to take a certain action.