DETReg: Unsupervised Pretraining with Region Priors for Object Detection

Technical Report

Amir Bar1   Xin Wang2   Vadim Kantorov1   Colorado J Reed2   Roei Herzig1
Gal Chechik3,4   Anna Rohrbach2   Trevor Darrell2   Amir Globerson1  
1Tel-Aviv University  2Berkeley AI Research  3Nvidia  4Bar-Ilan University



We present DETReg, an unsupervised pretraining approach for object DEtection with TRansformers using Region priors. Motivated by the two tasks underlying object detection: localization and categorization, we combine two complementary signals for self-supervision. For an object localization signal, we use pseudo ground truth object bounding boxes from an off-the-shelf unsupervised region proposal method, Selective Search, which does not require training data and can detect objects at a high recall rate and very low precision. The categorization signal comes from an object embedding loss that encourages invariant object representations, from which the object category can be inferred. We show how to combine these two signals to train the Deformable DETR detection architecture from large amounts of unlabeled data. DETReg improves the performance over competitive baselines and previous self-supervised methods on standard benchmarks like MS COCO and PASCAL VOC. DETReg also outperforms previous supervised and unsupervised baseline approaches on low-data regime when trained with only 1%, 2%, 5%, and 10% of the labeled data on MS COCO.

DETReg Pretext task

Our approach to the problem is based on the observation that learning good detectors requires learning to detect objects in the pretraining stage. To accomplish this, we present a new framework called ``DEtection with TRansformers based on Region priors'', or DETReg. DETReg can be used to train a detector on unlabeled data by introducing two key pretraining tasks: ``Object Localization Task'' and the ``Object Embedding Task''. The goal of the first is to train the model to localize objects, regardless of their categories. However, learning to localize objects is not enough, and detectors must also classify objects. Towards this end, we introduce the ``Object Embedding Task'', which is geared towards understanding the categories of objects in the image. Inspired by the simplicity of recent transformers for object detection, we choose to base our approach on the Deformable DETR architecture, which simplifies the implementation and is very fast to train.


Qualitative examples of DETReg unsupervised box predictions. This shows the pixel-level gradient norm for the x/y bounding box center and the object embedding. These gradient norms indicate how sensitive the predicted values are to perturbations of the input pixels. For the first three columns, DETReg attends to the object edges for the x/y predictions and z for the predicted object embedding. The final column shows a limitation where the space surrounding the object is used for the embedding.


DETReg: Unsupervised Pretraining with Region Priors for Object Detection
Amir Bar, Xin Wang, Vadim Kantorov, Colorado J Reed, Roei Herzig,
Gal Chechik, Anna Rohrbach, Trevor Darrell, Amir Globerson
Hosted on arXiv

Related Works

If you found our work interesting, please also consider looking into some closely related works like RegionSim, UP-DETR, SwAV.


We would like to thank Sayna Ebrahimi for helpful feedback and discussions. This project has received funding from the European Research Council (ERC) under the European Unions Horizon 2020 research and innovation programme (grant ERC HOLI 819080). Prof. Darrell’s group was supported in part by DoD including DARPA's XAI, LwLL, and/or SemaFor programs, as well as BAIR's industrial alliance programs. GC group was supported by the Israel Science Foundation (ISF 737/2018), and by an equipment grant to GC and Bar-Ilan University from the Israel Science Foundation (ISF 2332/18). This work was completed in partial fulfillment for the Ph.D degree of the first author.