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Part 1: Document Description
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Citation |
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Title: |
Image-Guided Object Detection using OWL-ViTand Enhanced Query Embedding Extraction |
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Identification Number: |
doi:10.7910/DVN/PRHQMK |
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Distributor: |
Harvard Dataverse |
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Date of Distribution: |
2024-04-14 |
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Version: |
1 |
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Bibliographic Citation: |
Melih Serin, 2024, "Image-Guided Object Detection using OWL-ViTand Enhanced Query Embedding Extraction", https://doi.org/10.7910/DVN/PRHQMK, Harvard Dataverse, V1 |
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Citation |
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Title: |
Image-Guided Object Detection using OWL-ViTand Enhanced Query Embedding Extraction |
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Identification Number: |
doi:10.7910/DVN/PRHQMK |
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Authoring Entity: |
Melih Serin (Boğaziçi University) |
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Distributor: |
Harvard Dataverse |
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Access Authority: |
Melih Serin |
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Depositor: |
KUUJE |
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Date of Deposit: |
2024-04-14 |
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Holdings Information: |
https://doi.org/10.7910/DVN/PRHQMK |
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Study Scope |
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Keywords: |
Engineering, Open-Vocabulary Object Detection with Vision Transformers (OWL-ViT), Object Detection, Vision Transformers, End-to-End Training, Generalized Intersection over Union (gIoU) Loss |
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Abstract: |
Computer vision has been receiving increasing attention with the recent complex Generative AI models released by tech industry giants, such as OpenAI and Google. However, there is a specific subfield that we wanted to focus on, that is, Image-Guided Object Detection. A detailed literature survey directed us towards a successful study called Simple Open-Vocabulary Object Detection with Vision Transformers (OWL-ViT) [1], which is a multifunctional complex model that can also perform image-guided object detection as a side function. In this study, some experiments have been conducted utilizing OWL-ViT architecture as the base model and manipulated the necessary parts to achieve a better one-shot performance. Code and models are available on GitHub. |
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Methodology and Processing |
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Sources Statement |
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Data Access |
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Other Study Description Materials |
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Label: |
ImageGuidedObjectDetection.pdf |
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Notes: |
application/pdf |