Memory-augmented Continual Detection Transformers (accepted in ECCV'24)
Preventing Catastrophic Forgetting through Memory Networks in Continuous Detection
Gaurav Bhatt*, James Ross, Leonid Sigal
$Abstract$ . Modern pre-trained architectures struggle to retain previous information while undergoing continuous fine-tuning on new tasks. Despite notable progress in continual classification, systems designed for complex vision tasks such as detection or segmentation still struggle to attain satisfactory performance. In this work, we introduce a memorybased detection transformer architecture to adapt a pre-trained DETRstyle detector to new tasks while preserving knowledge from previous tasks. We propose a novel localized query function for efficient information retrieval from memory units, aiming to minimize forgetting. Furthermore, we identify a fundamental challenge in continual detection referred to as background relegation. This arises when object categories from earlier tasks reappear in future tasks, potentially without labels, leading them to be implicitly treated as background. This is an inevitable issue in continual detection or segmentation. The introduced continual optimization technique effectively tackles this challenge. Finally, we assess the performance of our proposed system on continual detection benchmarks and demonstrate that our approach surpasses the performance of existing state-of-the-art resulting in 5-7% improvements on MS-COCO and PASCAL-VOC on the task of continual detection.
The code has been tested with the packages provided in the requirement.txt on Cuda 11.3 and Pytorch 1.12.1, etc. I have used pip freeze > requirement.txt to dump all the dependencies.
For MSCOCO provide the path to train, validation and annotation folder in the run.sh file. For quick pre-processing I have dumped all the annotation which can be downloaded from this link: saved checkpoints (the link also contains checkpoints, logs, validation detection samples). The folder that would be needed for training is upload/mscoco/ present in the uploaded folder. The task_ann_dir flag in run.sh should point to this folder.
$ pip install gdown
$ gdown 1FZW7HfxkftAqwkmDBa5CmxxVhpxWxJDKSet the flags in the run.sh file. The train flag should be 1.
$ bash run.shSet the flags and paths in the run.sh file. The train flag should be 0. Make sure to download all the checkpoints provided in the link: saved checkpoints
$ bash run.shIf you find this repo useful, please cite:
@article{bhatt2024preventing,
title={Preventing Catastrophic Forgetting through Memory Networks in Continuous Detection},
author={Bhatt, Gaurav and Ross, James and Sigal, Leonid},
journal={In ECCV 2024},
year={2024}
}
