(2013) 300 Faces in-the-Wild Challenge: the first facial landmark localization challenge. In: 2013 IEEE International

By Christos Sagonas, Georgios Tzimiropoulos, Stefanos P. Zafeiriou, Maja Pantic. 2017

Subfields: Computer Vision and Pattern Recognition, Evaluation & Benchmarks

How researchers see this paper

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

In Computer Vision and Pattern Recognition

1. Luc Van Gool
2. In-So Kweon
3. Yi Yang
4. Kaiming He
5. Dacheng Tao
6. Xiaogang Wang
7. Jitendra Malik
8. Ming-Hsuan Yang

In Evaluation & Benchmarks

1. Kaiming He
2. Shaoqing Ren
3. X. Zhang
4. Shiguo Lian
5. Jian‐Yuan Sun
6. Kaiyong Zhao
7. Yukyung Choi
8. Kai Wang

Citations in the corpus

• Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks (2015, methodology): “The mAP is nearly unchanged with N = 1k (55.8%), but degrades considerably to 44.6% whenN = 100.”
• Accelerating Very Deep Convolutional Networks for Classification and Detection (2015, background): “Index Terms—Convolutional Neural Networks, Acceleration, Image Classification, Object Detection F”
• Vision-based robotic grasping from object localization, object pose estimation to grasp estimation for parallel grippers: a review (2020, methodology): “, 2019] segments unknown 3d objects from real depth images using Mask R-CNN [He et al., 2017] trained on synthetic data.”
• Fast multiple objects detection and tracking fusing color camera and 3D LIDAR for intelligent vehicles (2016, methodology): “We use the Fast R-CNN [1] which is one of the state-of-the-art ob­ ject detection algorithms without training.”