# ASSD: Attentive Single Shot Multibox Detector

arXiv: https://arxiv.org/abs/1909.12456 · HTML: https://mlsanity.com/paper/1909.12456/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Sep 2019.

## Built on by (most cited first, 5 of 5)

- [ObjectBox: From Centers to Boxes for Anchor-Free Object Detection](https://mlsanity.com/paper/2207.06985/) (2022)
- [CvT-ASSD: Convolutional vision-Transformer Based Attentive Single Shot MultiBox Detector](https://mlsanity.com/paper/2110.12364/) (2021)
- [All You Need for Object Detection: From Pixels, Points, and Prompts to Next-Gen Fusion and Multimodal LLMs/VLMs in Autonomous Vehicles](https://mlsanity.com/paper/2510.26641/) (2025)
- [Recent Trends in 2D Object Detection and Applications in Video Event Recognition](https://mlsanity.com/paper/2202.03206/) (2022)
- [Multimodal Query-guided Object Localization](https://mlsanity.com/paper/2212.00749/) (2022)

## Abstract (arXiv)

This paper proposes a new deep neural network for object detection. The proposed network, termed ASSD, builds feature relations in the spatial space of the feature map. With the global relation information, ASSD learns to highlight useful regions on the feature maps while suppressing the irrelevant information, thereby providing reliable guidance for object detection. Compared to methods that rely on complicated CNN layers to refine the feature maps, ASSD is simple in design and is computationally efficient. Experimental results show that ASSD competes favorably with the state-of-the-arts, including SSD, DSSD, FSSD and RetinaNet.