October 22, 2022
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 trans- former that dynamically extracts correlations between the query image and the entire support set, instead of a single average-pooled support em- bedding. 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.
Written by
Naila Murray
Lei Wang
Piotr Koniusz
Shan Zhang
Publisher
ECCV
Research Topics
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