27 citations · 101 across the 12 of their papers we have counts for
12 papers
Active Poisoning: Efficient Backdoor Attacks on Transfer Learning-Based Brain-Computer Interfaces
X. Jiang, L. Meng, S. Li +1
Transfer learning (TL) has been widely used in electroencephalogram (EEG)-based brain-computer interfaces (BCIs) for reducing calibration efforts. However, backdoor attacks could b…
T-TIME: Test-Time Information Maximization Ensemble for Plug-and-Play BCIs
Siyang Li, Ziwei Wang, Hanbin Luo +2
Objective: An electroencephalogram (EEG)-based brain-computer interface (BCI) enables direct communication between the human brain and a computer. Due to individual differences and…
Facial Affect Analysis: Learning from Synthetic Data & Multi-Task Learning Challenges
Siyang Li, Yifan Xu, Huanyu Wu +4
Facial affect analysis remains a challenging task with its setting transitioned from lab-controlled to in-the-wild situations. In this paper, we present novel frameworks to handle…
The surprising impact of mask-head architecture on novel class segmentation
Vighnesh Birodkar, Zhichao Lu, Siyang Li +2
Instance segmentation models today are very accurate when trained on large annotated datasets, but collecting mask annotations at scale is prohibitively expensive. We address the p…
Low-Resource Machine Translation Training Curriculum Fit for Low-Resource Languages
Garry Kuwanto, Afra Feyza Akyürek, Isidora Chara Tourni +3
We conduct an empirical study of neural machine translation (NMT) for truly low-resource languages, and propose a training curriculum fit for cases when both parallel training data…
Unsupervised Video Object Segmentation with Distractor-Aware Online Adaptation
Ye Wang, Jongmoo Choi, Yueru Chen +5
Unsupervised video object segmentation is a crucial application in video analysis without knowing any prior information about the objects. It becomes tremendously challenging when…
Design Pseudo Ground Truth with Motion Cue for Unsupervised Video Object Segmentation
Ye Wang, Jongmoo Choi, Yueru Chen +4
One major technique debt in video object segmentation is to label the object masks for training instances. As a result, we propose to prepare inexpensive, yet high quality pseudo g…
Interpretable Convolutional Neural Networks via Feedforward Design
C. -C. Jay Kuo, Min Zhang, Siyang Li +2
The model parameters of convolutional neural networks (CNNs) are determined by backpropagation (BP). In this work, we propose an interpretable feedforward (FF) design without any B…
Instance Embedding Transfer to Unsupervised Video Object Segmentation
Siyang Li, Bryan Seybold, Alexey Vorobyov +3
We propose a method for unsupervised video object segmentation by transferring the knowledge encapsulated in image-based instance embedding networks. The instance embedding network…
Multiple Instance Curriculum Learning for Weakly Supervised Object Detection
Siyang Li, Xiangxin Zhu, Qin Huang +2
When supervising an object detector with weakly labeled data, most existing approaches are prone to trapping in the discriminative object parts, e.g., finding the face of a cat ins…
A Taught-Obesrve-Ask (TOA) Method for Object Detection with Critical Supervision
Chi-Hao Wu, Qin Huang, Siyang Li +1
Being inspired by child's learning experience - taught first and followed by observation and questioning, we investigate a critically supervised learning methodology for object det…
Semantic Segmentation with Reverse Attention
Qin Huang, Chunyang Xia, Chihao Wu +4
Recent development in fully convolutional neural network enables efficient end-to-end learning of semantic segmentation. Traditionally, the convolutional classifiers are taught to…