134 citations · 920 across the 72 of their papers we have counts for
7 papers · 1 filter
A Knowledge-Driven Quality-of-Experience Model for Adaptive Streaming Videos
Zhengfang Duanmu, Wentao Liu, Diqi Chen +4
The fundamental conflict between the enormous space of adaptive streaming videos and the limited capacity for subjective experiment casts significant challenges to objective Qualit…
MVP-Net: Multi-view FPN with Position-aware Attention for Deep Universal Lesion Detection
Zihao Li, Shu Zhang, Junge Zhang +3
Universal lesion detection (ULD) on computed tomography (CT) images is an important but underdeveloped problem. Recently, deep learning-based approaches have been proposed for ULD,…
L_DMI: An Information-theoretic Noise-robust Loss Function
Yilun Xu, Peng Cao, Yuqing Kong +1
Accurately annotating large scale dataset is notoriously expensive both in time and in money. Although acquiring low-quality-annotated dataset can be much cheaper, it often badly d…
Multi-level Domain Adaptive learning for Cross-Domain Detection
Rongchang Xie, Fei Yu, Jiachao Wang +2
In recent years, object detection has shown impressive results using supervised deep learning, but it remains challenging in a cross-domain environment. The variations of illuminat…
Max-MIG: an Information Theoretic Approach for Joint Learning from Crowds
Peng Cao, Yilun Xu, Yuqing Kong +1
Eliciting labels from crowds is a potential way to obtain large labeled data. Despite a variety of methods developed for learning from crowds, a key challenge remains unsolved: \em…
-LBI: Stochastic Split Linearized Bregman Iterations for Parsimonious Deep Learning
Yanwei Fu, Donghao Li, Xinwei Sun +3
This paper proposes a novel Stochastic Split Linearized Bregman Iteration (-LBI) algorithm to efficiently train the deep network. The -LBI introduces an iterative reg…
Multi-Agent Tensor Fusion for Contextual Trajectory Prediction
Tianyang Zhao, Yifei Xu, Mathew Monfort +5
Accurate prediction of others' trajectories is essential for autonomous driving. Trajectory prediction is challenging because it requires reasoning about agents' past movements, so…