mlsanityData to 14 Oct 2026

DPNET: Dual-Path Network for Efficient Object Detectioj with Lightweight Self-Attention

arXiv 2111.00500 ↗First posted 31 Oct 2021cs.CVcited by 1

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Abstract (arXiv)

Object detection often costs a considerable amount of computation to get satisfied performance, which is unfriendly to be deployed in edge devices. To address the trade-off between computational cost and detection accuracy, this paper presents a dual path network, named DPNet, for efficient object detection with lightweight self-attention. In backbone, a single input/output lightweight self-attention module (LSAM) is designed to encode global interactions between different positions. LSAM is also extended into a multiple-inputs version in feature pyramid network (FPN), which is employed to capture cross-resolution dependencies in two paths. Extensive experiments on the COCO dataset demonstrate that our method achieves state-of-the-art detection results. More specifically, DPNet obtains 29.0% AP on COCO test-dev, with only 1.14 GFLOPs and 2.27M model size for a 320x320 image.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 33 of its 35 references that are in the map. First posted on arXiv 31 Oct 2021.

Builds on

33 of 35 references.

References · 33

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21 more
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