Cluster and Aggregate: Face Recognition with Large Probe Set
Authors: Minchul Kim, Feng Liu, Anil K Jain, Xiaoming Liu
NeurIPS 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on IJB-B and IJB-S benchmark datasets show the superiority of the proposed two-stage paradigm in unconstrained face recognition. |
| Researcher Affiliation | Academia | Minchul Kim Department of Computer Science Michigan State University East Lansing, MI 48824 kimminc2@msu.edu Feng Liu Department of Computer Science Michigan State University East Lansing, MI 48824 liufeng6@msu.edu Anil Jain Department of Computer Science Michigan State University East Lansing, MI 48824 jain@msu.edu Xiaoming Liu Department of Computer Science Michigan State University East Lansing, MI 48824 liuxm@msu.edu |
| Pseudocode | No | The paper describes the proposed approach with text and diagrams but does not include structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Code and pretrained models are available in Link. |
| Open Datasets | Yes | We use Web Face4M [57] as our training dataset. It is a large-scale dataset with 4.2M facial images from 205, 990 identities. ... We test on IJB-B [51], IJB-C [35] and IJB-S [21] datasets. |
| Dataset Splits | No | We use Web Face4M [57] as our training dataset. ... We test on IJB-B [51], IJB-C [35] and IJB-S [21] datasets. ... For IJB-S, we use protocols, Surv.-to-Single, Surv.-to-Booking and Surv.-to-Surv. (No explicit train/validation/test splits defined for their overall experimental setup). |
| Hardware Specification | No | The paper discusses computation efficiency and GPU memory usage but does not provide specific details on the hardware used for experiments, such as GPU/CPU models or memory specifications. |
| Software Dependencies | No | The paper mentions software components and models like 'IRes Net-101' and 'Arc Face loss' but does not specify version numbers for any software dependencies or libraries used in the experiments. |
| Experiment Setup | Yes | The training hyper-parameters such as optimizers are detailed in Supp.A. |