Solving the Families In the Wild Kinship Verification Challenge by Program Synthesis
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Abstract (arXiv)
Kinship verification is the task of determining whether a parent-child, sibling, or grandparent-grandchild relationship exists between two people and is important in social media applications, forensic investigations, finding missing children, and reuniting families. We demonstrate high quality kinship verification by participating in the 2021 Recognizing Families in the Wild challenge which provides the largest publicly available dataset in the field. Our approach is among the top 3 winning entries in the competition. We ensemble models written by both human experts and a foundation model, OpenAI Codex, trained on text and code. We use Codex to generate model variants, and also demonstrate its ability to generate entire running programs for kinship verification tasks of specific relationships.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 9 of its 25 references that are in the map. First posted on arXiv 13 Oct 2021.
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References · 9
- 2015Deep Residual Learning for Image Recognitioncited by 51,809
- 2014Very Deep Convolutional Networks for Large-Scale Image Recognitioncited by 15,267
- 2020A Simple Framework for Contrastive Learning of Visual Representationscited by 11,979
- 2021Evaluating Large Language Models Trained on Codecited by 8,053
- 2017Squeeze-and-Excitation Networkscited by 4,763
- 2015FaceNet: A Unified Embedding for Face Recognition and Clusteringcited by 2,833
- 2016Families in the Wild (FIW): Large-Scale Kinship Image Database and Benchmarkscited by 13
- 2020Deep Fusion Siamese Network for Automatic Kinship Verificationcited by 7
- 2020Recognizing Families In the Wild: White Paper for the 4th Edition Data Challengecited by 4
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