mlsanityData to 7 Oct 2026

A Cross-direction Task Decoupling Network for Small Logo Detection

arXiv 2305.02503 ↗First posted 4 May 2023cs.CVcited by 0

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Abstract (arXiv)

Logo detection plays an integral role in many applications. However, handling small logos is still difficult since they occupy too few pixels in the image, which burdens the extraction of discriminative features. The aggregation of small logos also brings a great challenge to the classification and localization of logos. To solve these problems, we creatively propose Cross-direction Task Decoupling Network (CTDNet) for small logo detection. We first introduce Cross-direction Feature Pyramid (CFP) to realize cross-direction feature fusion by adopting horizontal transmission and vertical transmission. In addition, Multi-frequency Task Decoupling Head (MTDH) decouples the classification and localization tasks into two branches. A multi frequency attention convolution branch is designed to achieve more accurate regression by combining discrete cosine transform and convolution creatively. Comprehensive experiments on four logo datasets demonstrate the effectiveness and efficiency of the proposed method.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 27 of its 32 references that are in the map. First posted on arXiv 4 May 2023.

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27 of 32 references.

References · 27

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15 more
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  15. 2021Discriminative Semantic Feature Pyramid Network with Guided Anchoring for Logo Detectioncited by 1

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