104 citations · 192 across the 9 of their papers we have counts for
9 papers
Accelerating Discrete Diffusion Models with Parallel-In-Time Sampling
Yu Yao, Huanjian Zhou, Andi Han +2
Discrete diffusion models are widely used for learning and generating discrete distributions. As the generation process is inherently sequential, the acceleration of sampling is of…
PNT-Edge: Towards Robust Edge Detection with Noisy Labels by Learning Pixel-level Noise Transitions
Wenjie Xuan, Shanshan Zhao, Yu Yao +5
Relying on large-scale training data with pixel-level labels, previous edge detection methods have achieved high performance. However, it is hard to manually label edges accurately…
A Many-ported and Shared Memory Architecture for High-Performance ADAS SoCs
Hao Luan, Yu Yao, Chang Huang
Increasing investment in computing technologies and the advancements in silicon technology has fueled rapid growth in advanced driver assistance systems (ADAS) and corresponding So…
Multi-scale Cooperative Multimodal Transformers for Multimodal Sentiment Analysis in Videos
Lianyang Ma, Yu Yao, Tao Liang +1
Multimodal sentiment analysis in videos is a key task in many real-world applications, which usually requires integrating multimodal streams including visual, verbal and acoustic b…
Do We Need to Penalize Variance of Losses for Learning with Label Noise?
Yexiong Lin, Yu Yao, Yuxuan Du +4
Algorithms which minimize the averaged loss have been widely designed for dealing with noisy labels. Intuitively, when there is a finite training sample, penalizing the variance of…
Instance-dependent Label-noise Learning under a Structural Causal Model
Yu Yao, Tongliang Liu, Mingming Gong +3
Label noise will degenerate the performance of deep learning algorithms because deep neural networks easily overfit label errors. Let X and Y denote the instance and clean label, r…
Dual T: Reducing Estimation Error for Transition Matrix in Label-noise Learning
Yu Yao, Tongliang Liu, Bo Han +4
The transition matrix, denoting the transition relationship from clean labels to noisy labels, is essential to build statistically consistent classifiers in label-noise learning. E…
When, Where, and What? A New Dataset for Anomaly Detection in Driving Videos
Yu Yao, Xizi Wang, Mingze Xu +3
Video anomaly detection (VAD) has been extensively studied. However, research on egocentric traffic videos with dynamic scenes lacks large-scale benchmark datasets as well as effec…
Rethinking Class-Prior Estimation for Positive-Unlabeled Learning
Yu Yao, Tongliang Liu, Bo Han +4
Given only positive (P) and unlabeled (U) data, PU learning can train a binary classifier without any negative data. It has two building blocks: PU class-prior estimation (CPE) and…