3 citations · 3 across the 3 of their papers we have counts for
3 papers
Improving Open-World Object Localization by Discovering Background
Ashish Singh, Michael J. Jones, Kuan-Chuan Peng +3
Our work addresses the problem of learning to localize objects in an open-world setting, i.e., given the bounding box information of a limited number of object classes during train…
Mod-Squad: Designing Mixture of Experts As Modular Multi-Task Learners
Zitian Chen, Yikang Shen, Mingyu Ding +4
Optimization in multi-task learning (MTL) is more challenging than single-task learning (STL), as the gradient from different tasks can be contradictory. When tasks are related, it…
Background subtraction - separating the modeling and the inference
Manjunath Narayana, Allen Hanson, Erik Learned-Miller
In its early implementations, background modeling was a process of building a model for the background of a video with a stationary camera, and identifying pixels that did not conf…