Awesome

SOR

Implementation of "Salient Object Ranking with Position-Preserved Attention"

Salient Object Ranking with Position-Preserved Attention
Hao Fang, Daoxin Zhang, Yi Zhang, Minghao Chen, Jiawei Li, Yao Hu, Deng Cai, Xiaofei He
Arxiv Preprint 2106

Abstract

Instance segmentation can detect where the objects are in an image, but hard to understand the relationship between them. We pay attention to a typical relationship, relative saliency. A closely related task, salient object detection, predicts a binary map highlighting a visually salient region while hard to distinguish multiple objects. Directly combining two tasks by post-processing also leads to poor performance. There is a lack of research on relative saliency at present, limiting the practical applications such as content-aware image cropping, video summary, and image labeling. In this paper, we study the Salient Object Ranking (SOR) task, which manages to assign a ranking order of each detected object according to its visual saliency. We propose the first end-to-end framework of the SOR task and solve it in a multi-task learning fashion. The framework handles instance segmentation and salient object ranking simultaneously. In this framework, the SOR branch is independent and flexible to cooperate with different detection methods, so that easy to use as a plugin. We also introduce a Position-Preserved Attention (PPA) module tailored for the SOR branch. It consists of the position embedding stage and feature interaction stage. Considering the importance of position in saliency comparison, we preserve absolute coordinates of objects in ROI pooling operation and then fuse positional information with semantic features in the first stage. In the feature interaction stage, we apply the attention mechanism to obtain proposals' contextualized representations to predict their relative ranking orders. Extensive experiments have been conducted on the ASR dataset. Without bells and whistles, our proposed method outperforms the former state-of-the-art method significantly. The code will be released publicly available on https://github.com/EricFH/SOR.

Framework

Detail

1. Clone the repository

git clone git@github.com:EricFH/SOR.git

2. Install dependencies

This project is based on centermask2. There are many pretrained models can be used in this project as backbone of network.

The main dependency is detectron2, a concise detection framework. Please follow the installation tutorial.

3. Data preparation

Download ASSR dataset from goole drive and extract it, then link or move ASSR to sor_ppa/datasets/ASSR

4. Training

python train_sor.py --config-file configs/sor/ppa.yaml --num-gpus 2

5. Testing

python train_sor.py --config-file configs/sor/ppa.yaml --eval-only MODEL.WEIGHTS path/to/model

There is a pretrained model (centermask-39) release on google drive

Demo

Talk

Youtube

Citation

@InProceedings{Fang_2021_ICCV,
    author    = {Fang, Hao and Zhang, Daoxin and Zhang, Yi and Chen, Minghao and Li, Jiawei and Hu, Yao and Cai, Deng and He, Xiaofei},
    title     = {Salient Object Ranking With Position-Preserved Attention},
    booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
    month     = {October},
    year      = {2021},
    pages     = {16331-16341}
}