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arXiv · 2210.16897

Time-rEversed diffusioN tEnsor Transformer: A new TENET of Few-Shot Object Detection

Shan Zhang·Naila Murray·Lei Wang·Piotr Koniusz
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Abstract

In this paper, we tackle the challenging problem of Few-shot Object Detection. Existing FSOD pipelines (i) use average-pooled representations that result in information loss; and/or (ii) discard position information that can help detect object instances. Consequently, such pipelines are sensitive to large intra-class appearance and geometric variations between support and query images. To address these drawbacks, we propose a Time-rEversed diffusioN tEnsor Transformer (TENET), which i) forms high-order tensor representations that capture multi-way feature occurrences that are highly discriminative, and ii) uses a transformer that dynamically extracts correlations between the query image and the entire support set, instead of a single average-pooled support embedding. We also propose a Transformer Relation Head (TRH), equipped with higher-order representations, which encodes correlations between query regions and the entire support set, while being sensitive to the positional variability of object instances. Our model achieves state-of-the-art results on PASCAL VOC, FSOD, and COCO.

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Shan Zhang, Naila Murray, Lei Wang, Piotr Koniusz. 2022-10-30. Time-rEversed diffusioN tEnsor Transformer: A new TENET of Few-Shot Object Detection. https://arxiv.org/abs/2210.16897

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