by Chi, Cheng and Wei, Fangyun and Hu, Han

Existing object detection frameworks are usually built on a single format of objject/part representation, i.e., anchor/proposal rectangle boxes in RetinaNet and Faster R-CNN, center points in FCOS and RepPoints, and corner points in CornerNet. While these different representations usually drive the frameworks to perform well in different aspects, e.g., better classification or finer localization, it is in general difficult to combine these representations in a single framework to make good use of each strength, due to the heterogeneous or non-grid feature extraction by different representations. This paper presents an attention-based decoder module similar as that in Transformer to bridge other representations into a typical object detector built on a single representation format, in an end-to-end fashion. The other representations act as a set of key instances to strengthen the main query representation features in the vanilla detectors. Novel techniques are proposed towards efficient computation of the decoder module, including a key sampling approach and a shared location embedding approach. The proposed module is named bridging visual representations (BVR).

• Install it

bash install.sh ${your_code_dir}
cd ${your_code_dir}
mkdir -p data 
ln -s ${your_coco_path} data/coco

where your_code_dir is your code path and your_coco_path is the location of extracted coco dataset on your server. For more information, you may refer to getting started

• For testing

bash tools/dist_test.sh ${selected_config} 8

where selected_config is one of provided script under the config/bvr folder.

• For training

bash tools/dist_train.sh ${selected_config} 8

where selected_config is one of provided script under the config/bvr folder.

• For more dataset

We have not trained or tested on other dataset. If you would like to use it on other data, please refer to mmdetection.

@inproceedings{relationnetplusplus2020,
  title={RelationNet++: Bridging Visual Representations for Object Detection via Transformer Decoder},
  author={Chi, Cheng and Wei, Fangyun and Hu, Han},
  booktitle={NeurIPS},
  year={2020}
}

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