Individualness and Determinantal Point Processes for Pedestrian Detection

By Donghoon Lee, Geonho Cha, Ming-Hsuan Yang, Songhwai Oh. 2016

37 citations recorded by the source corpus.

Subfields: Applied Mathematics, Computer Vision and Pattern Recognition, Ecology

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Applied Mathematics

1. Hedvig Kjellström
2. Cheng Zhang
3. Stephan Mandt

In Computer Vision and Pattern Recognition

1. Songtao Liu
2. Di Huang
3. Yunhong Wang
4. Hedvig Kjellström
5. Cheng Zhang
6. Stephan Mandt
7. Jian‐Yuan Sun
8. Ling Shao

In Ecology

1. Hedvig Kjellström
2. Cheng Zhang
3. Stephan Mandt

Citations in the corpus

• Balanced Mini-batch Sampling for SGD Using Determinantal Point Processes (2017, methodology): “The DPP [19, 25] has been proposed [20, 22, 45] and advanced [4, 23, 24] in the machine learning community in the recent years.”
• Adaptive NMS: Refining Pedestrian Detection in a Crowd (2019, methodology): “[18] extends the optimization model with individualness scores, which relies on discriminative CNN features.”
• Stochastic Learning on Imbalanced Data: Determinantal Point Processes for Mini-batch Diversification (2017, methodology): “The DPP [19, 25] has been proposed [20, 22, 44] and advanced [4, 23, 24] in the machine learning community in the recent years.”