Cluster and Aggregate: Face Recognition with Large Probe Set
Authors: Minchul Kim, Feng Liu, Anil K Jain, Xiaoming Liu
NeurIPS 2022 | Venue 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 EMAIL Feng Liu Department of Computer Science Michigan State University East Lansing, MI 48824 EMAIL Anil Jain Department of Computer Science Michigan State University East Lansing, MI 48824 EMAIL Xiaoming Liu Department of Computer Science Michigan State University East Lansing, MI 48824 EMAIL |
| 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. |