# AttentionMask: Attentive, Efficient Object Proposal Generation Focusing on Small Objects

arXiv: https://arxiv.org/abs/1811.08728 · HTML: https://mlsanity.com/paper/1811.08728/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Nov 2018.

## Built on by (most cited first, 9 of 9)

- [A Guide to Image and Video based Small Object Detection using Deep Learning : Case Study of Maritime Surveillance](https://mlsanity.com/paper/2207.12926/) (2022)
- [Deep Learning for Logo Detection: A Survey](https://mlsanity.com/paper/2210.04399/) (2022)
- [Localizing Small Apples in Complex Apple Orchard Environments](https://mlsanity.com/paper/2202.11372/) (2022)
- [Superpixel-based Refinement for Object Proposal Generation](https://mlsanity.com/paper/2101.04574/) (2021)
- [DeepFH Segmentations for Superpixel-based Object Proposal Refinement](https://mlsanity.com/paper/2108.03503/) (2021)
- [SOS: Segment Object System for Open-World Instance Segmentation With Object Priors](https://mlsanity.com/paper/2409.14627/) (2024)
- [Small, but important: Traffic light proposals for detecting small traffic lights and beyond](https://mlsanity.com/paper/2307.15191/) (2023)
- [Segmenting Medical Instruments in Minimally Invasive Surgeries using AttentionMask](https://mlsanity.com/paper/2203.11358/) (2022)
- [DenseScout: Algorithm-System Co-design for Budgeted Tiny Object Selection on Edge Platforms](https://mlsanity.com/paper/2604.25300/) (2026)

## Abstract (arXiv)

We propose a novel approach for class-agnostic object proposal generation, which is efficient and especially well-suited to detect small objects. Efficiency is achieved by scale-specific objectness attention maps which focus the processing on promising parts of the image and reduce the amount of sampled windows strongly. This leads to a system, which is 33% faster than the state-of-the-art and clearly outperforming state-of-the-art in terms of average recall. Secondly, we add a module for detecting small objects, which are often missed by recent models. We show that this module improves the average recall for small objects by about 53%.