# Single-Shot Object Detection with Enriched Semantics

arXiv: https://arxiv.org/abs/1712.00433 · HTML: https://mlsanity.com/paper/1712.00433/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Dec 2017.

## Built on by (most cited first, 14 of 14)

- [QueryDet: Cascaded Sparse Query for Accelerating High-Resolution Small Object Detection](https://mlsanity.com/paper/2103.09136/) (2021)
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- [Remote Sensing Object Detection Meets Deep Learning: A Meta-review of Challenges and Advances](https://mlsanity.com/paper/2309.06751/) (2023)
- [Circle Representation for Medical Object Detection](https://mlsanity.com/paper/2110.12093/) (2021)
- [Few-Shot Video Object Detection](https://mlsanity.com/paper/2104.14805/) (2021)
- [Joint Distribution Alignment via Adversarial Learning for Domain Adaptive Object Detection](https://mlsanity.com/paper/2109.09033/) (2021)
- [Relation-Guided Adversarial Learning for Data-free Knowledge Transfer](https://mlsanity.com/paper/2412.11380/) (2024)
- [Semantic Attention and Scale Complementary Network for Instance Segmentation in Remote Sensing Images](https://mlsanity.com/paper/2107.11758/) (2021)
- [Densely Semantic Enhancement for Domain Adaptive Region-free Detectors](https://mlsanity.com/paper/2108.13101/) (2021)
- [Adversarial Machine Learning in Text Analysis and Generation](https://mlsanity.com/paper/2101.08675/) (2021)
- [Tea Chrysanthemum Detection under Unstructured Environments Using the TC-YOLO Model](https://mlsanity.com/paper/2111.02724/) (2021)
- [Objects as Spatio-Temporal 2.5D points](https://mlsanity.com/paper/2212.02755/) (2022)
- [3DPillars: Pillar-based two-stage 3D object detection](https://mlsanity.com/paper/2509.05780/) (2025)
- [An Efficient and Generalizable Transfer Learning Method for Weather Condition Detection on Ground Terminals](https://mlsanity.com/paper/2511.00211/) (2025)

## Abstract (arXiv)

We propose a novel single shot object detection network named Detection with Enriched Semantics (DES). Our motivation is to enrich the semantics of object detection features within a typical deep detector, by a semantic segmentation branch and a global activation module. The segmentation branch is supervised by weak segmentation ground-truth, i.e., no extra annotation is required. In conjunction with that, we employ a global activation module which learns relationship between channels and object classes in a self-supervised manner. Comprehensive experimental results on both PASCAL VOC and MS COCO detection datasets demonstrate the effectiveness of the proposed method. In particular, with a VGG16 based DES, we achieve an mAP of 81.7 on VOC2007 test and an mAP of 32.8 on COCO test-dev with an inference speed of 31.5 milliseconds per image on a Titan Xp GPU. With a lower resolution version, we achieve an mAP of 79.7 on VOC2007 with an inference speed of 13.0 milliseconds per image.