MS-Celeb-1M: Challenge of Recognizing One Million Celebrities in the Real World

By Yandong Guo, Lei Zhang, Yuxiao Hu, Xiaodong He, Jianfeng Gao. 2016

92 citations recorded by the source corpus.

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. Xiaogang Wang
3. Dacheng Tao
4. Andrew Zisserman
5. Trevor Darrell
6. Ming-Hsuan Yang
7. Jitendra Malik
8. Bernt Schiele

In Evaluation & Benchmarks

1. Arsha Nagrani
2. Andrew Zisserman
3. Samuel Albanie
4. Emily L. Denton
5. Junjie Yan
6. Inioluwa Deborah Raji
7. Lei Zhang
8. Margaret Mitchell

Citations in the corpus

• Saving Face: Investigating the Ethical Concerns of Facial Recognition Auditing (2020, methodology): “We estimate the full list of celebrity names used in the Microsoft classifier through a publicly released dataset from the company which includes 100,000 logged celebrity identities [21].”
• MS-Celeb-1M: A Dataset and Benchmark for Large-Scale Face Recognition (2016, methodology): “The first step is to select a subset of entities (from freebase [5]) which correspond to real people using the criteria in [1].”
• Turning a Blind Eye: Explicit Removal of Biases and Variation from Deep Neural Network Embeddings (2018, background): “Large publicly available face datasets are often composed of celebrities, such as [2, 8, 10, 11, 12,16].”
• Shift: A Zero FLOP, Zero Parameter Alternative to Spatial Convolutions (2017, background): “We test on 6,000 pairs from LFW, 5,000 pairs from YTF and 100,000 pairs randomly generated from MSC, excluding the training set.”
• One-shot Face Recognition by Promoting Underrepresented Classes (2017, background): “This is one of the largest public available datasets [11, 10, 25, 32, 6, 21], which makes our model reproducible and meaningful.”
• Learnable PINs: Cross-Modal Embeddings for Person Identity (2018, methodology): “State of the art methods for face recognition use face embeddings generated by a deep convolutional neural network [33, 34, 39] trained on a large-scale dataset of labelled faces [8, 15, 19].”
• Knowledge Distillation via Route Constrained Optimization (2019, methodology): “We take two popular face recognition datasets MSCeleb-1M [5] and IMDb-Face [27] as our training set and validate our method on MegaFace.”
• Impact of Fully Connected Layers on Performance of Convolutional Neural Networks for Image Classification (2019, background): “(iii) Adding label noise leads to better performance in deep neural networks. Some attempts have also been made to analyze the impact of label noise in the training set for deep neural networks [12], [13]. Recently, Ferrari et al. [14] performed the analysis over many aspects that affect the perform”