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Transfer Learning for Olfactory Object Detection

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arxiv 2301.09906 v1 pith:VFW5IJN2 submitted 2023-01-24 cs.CV

Mathias Zinnen , Prathmesh Madhu , Peter Bell , Andreas Maier , Vincent Christlein This is my paper
classification cs.CV
keywords detectiondatasetsexperimentsobjectpretrainingstyleadditionalcategories
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We investigate the effect of style and category similarity in multiple datasets used for object detection pretraining. We find that including an additional stage of object-detection pretraining can increase the detection performance considerably. While our experiments suggest that style similarities between pre-training and target datasets are less important than matching categories, further experiments are needed to verify this hypothesis.

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  1. Smelly, dense, and spreaded: The Object Detection for Olfactory References (ODOR) dataset

    cs.CV 2025-07 conditional novelty 5.0 of 10

    The ODOR dataset contributes 38,116 fine-grained object annotations over 4,712 artworks, benchmarked with five detector families, to stress-test object detection on dense, occluded, and off-centre objects in historica...

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