# Task Specific Attention is one more thing you need for object detection

arXiv: https://arxiv.org/abs/2202.09048 · HTML: https://mlsanity.com/paper/2202.09048/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 22 references that are in the map.
- First posted on arXiv 18 Feb 2022.

## Builds on (most cited first, 18 of 18)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [End-to-End Object Detection with Transformers](https://mlsanity.com/paper/2005.12872/) (2020)
- [You Only Look Once: Unified, Real-Time Object Detection](https://mlsanity.com/paper/1506.02640/) (2015)
- [Training data-efficient image transformers & distillation through attention](https://mlsanity.com/paper/2012.12877/) (2020)
- [Deformable DETR: Deformable Transformers for End-to-End Object Detection](https://mlsanity.com/paper/2010.04159/) (2020)
- [Aggregated Residual Transformations for Deep Neural Networks](https://mlsanity.com/paper/1611.05431/) (2016)
- [Deformable Convolutional Networks](https://mlsanity.com/paper/1703.06211/) (2017)
- [MMDetection: Open MMLab Detection Toolbox and Benchmark](https://mlsanity.com/paper/1906.07155/) (2019)
- [Cascade R-CNN: Delving into High Quality Object Detection](https://mlsanity.com/paper/1712.00726/) (2017)
- [Bridging the Gap Between Anchor-based and Anchor-free Detection via Adaptive Training Sample Selection](https://mlsanity.com/paper/1912.02424/) (2019)
- [Dynamic Head: Unifying Object Detection Heads with Attentions](https://mlsanity.com/paper/2106.08322/) (2021)
- [Simple multi-dataset detection](https://mlsanity.com/paper/2102.13086/) (2021)
- [Revisiting the Sibling Head in Object Detector](https://mlsanity.com/paper/2003.07540/) (2020)
- [RepPoints V2: Verification Meets Regression for Object Detection](https://mlsanity.com/paper/2007.08508/) (2020)

## Built on by (most cited first, 2 of 2)

- [Multi-Cohort Framework with Cohort-Aware Attention and Adversarial Mutual-Information Minimization for Whole Slide Image Classification](https://mlsanity.com/paper/2409.11119/) (2024)
- [Scale-Invariant Object Detection by Adaptive Convolution with Unified Global-Local Context](https://mlsanity.com/paper/2410.05274/) (2024)

## Abstract (arXiv)

Various models have been proposed to perform object detection. However, most require many handdesigned components such as anchors and non-maximum-suppression(NMS) to demonstrate good performance. To mitigate these issues, Transformer-based DETR and its variant, Deformable DETR, were suggested. These have solved much of the complex issue in designing a head for object detection models; however, doubts about performance still exist when considering Transformer-based models as state-of-the-art methods in object detection for other models depending on anchors and NMS revealed better results. Furthermore, it has been unclear whether it would be possible to build an end-to-end pipeline in combination only with attention modules, because the DETR-adapted Transformer method used a convolutional neural network (CNN) for the backbone body. In this study, we propose that combining several attention modules with our new Task Specific Split Transformer (TSST) is a powerful method to produce the state-of-the art performance on COCO results without traditionally hand-designed components. By splitting the general-purpose attention module into two separated goal-specific attention modules, the proposed method allows for the design of simpler object detection models. Extensive experiments on the COCO benchmark demonstrate the effectiveness of our approach. Code is available at https://github.com/navervision/tsst