Publications (5)
Humas: A Heterogeneity- and Upgrade-aware Microservice Auto-scaling Framework in Large-scale Data Centers
Qin Hua, Dingyu Yang, Shiyou Qian +3
An effective auto-scaling framework is essential for microservices to ensure performance stability and resource efficiency under dynamic workloads. As revealed by many prior studie…
Nested Annealed Training Scheme for Generative Adversarial Networks
Chang Wan, Ming-Hsuan Yang, Minglu Li +2
Recently, researchers have proposed many deep generative models, including generative adversarial networks(GANs) and denoising diffusion models. Although significant breakthroughs…
PD-SORT: Occlusion-Robust Multi-Object Tracking Using Pseudo-Depth Cues
Yanchao Wang, Dawei Zhang, Run Li +2
Multi-object tracking (MOT) is a rising topic in video processing technologies and has important application value in consumer electronics. Currently, tracking-by-detection (TBD) i…
SAMOFT: Robust Multi-Object Tracking via Region and Flow
Yanchao Wang, Dawei Zhang, Chengzhuan Yang +5
Multi-object tracking (MOT) is a fundamental task in computer vision that requires continuously tracking multiple targets while maintaining consistent identities across frames. How…
A New Formulation of Lipschitz Constrained With Functional Gradient Learning for GANs
Chang Wan, Ke Fan, Xinwei Sun +4
This paper introduces a promising alternative method for training Generative Adversarial Networks (GANs) on large-scale datasets with clear theoretical guarantees. GANs are typical…