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MABNet: Master Assistant Buddy Network with Hybrid Learning for Image Retrieval

arXiv 2303.03050 ↗First posted 6 Mar 2023cs.CV cs.AI cs.IRcited by 0

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Abstract (arXiv)

Image retrieval has garnered growing interest in recent times. The current approaches are either supervised or self-supervised. These methods do not exploit the benefits of hybrid learning using both supervision and self-supervision. We present a novel Master Assistant Buddy Network (MABNet) for image retrieval which incorporates both learning mechanisms. MABNet consists of master and assistant blocks, both learning independently through supervision and collectively via self-supervision. The master guides the assistant by providing its knowledge base as a reference for self-supervision and the assistant reports its knowledge back to the master by weight transfer. We perform extensive experiments on public datasets with and without post-processing.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 31 of its 47 references that are in the map. First posted on arXiv 6 Mar 2023.

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31 of 47 references.

References · 31

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19 more
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