• Corpus ID: 236956437

Contrast R-CNN for Continual Learning in Object Detection

@article{Zheng2021ContrastRF,
  title={Contrast R-CNN for Continual Learning in Object Detection},
  author={Kai Zheng and Cen Chen},
  journal={ArXiv},
  year={2021},
  volume={abs/2108.04224}
}
The continual learning problem has been widely studied in image classification, while rare work has been explored in object detection. Some recent works apply knowledge distillation to constrain the model to retain old knowledge, but this rigid constraint is detrimental for learning new knowledge. In our paper, we propose a new scheme for continual learning of object detection, namely Contrast R-CNN, an approach strikes a balance between retaining the old knowledge and learning the new… 

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References

SHOWING 1-10 OF 34 REFERENCES

An End-to-End Architecture for Class-Incremental Object Detection with Knowledge Distillation

This paper systematically studies the task of learning an end-to-end class-incremental object detection model and proposes a Class-Incremental Faster R-CNN (CIFRCN) model that can dynamically add new classes by only using a few labeled images of new objects.

RILOD: near real-time incremental learning for object detection at the edge

An efficient yet practical system to incrementally train an existing object detection model such that it can detect new object classes without losing its capability to detect old classes, implemented under both edge-cloud and edge-only setups.

Cascade R-CNN: Delving Into High Quality Object Detection

A simple implementation of the Cascade R-CNN is shown to surpass all single-model object detectors on the challenging COCO dataset, and experiments show that it is widely applicable across detector architectures, achieving consistent gains independently of the baseline detector strength.

A Simple Semi-Supervised Learning Framework for Object Detection

STAC is proposed, a simple yet effective SSL framework for visual object detection along with a data augmentation strategy that deploys highly confident pseudo labels of localized objects from an unlabeled image and updates the model by enforcing consistency via strong augmentations.

Incremental Learning of Object Detectors without Catastrophic Forgetting

This work presents a method to learn object detectors incrementally, when neither the original training data nor annotations for the original classes in the new training set are available, and presents object detection results on the PASCAL VOC 2007 and COCO datasets.

CSPNet: A New Backbone that can Enhance Learning Capability of CNN

The proposed CSPNet respects the variability of the gradients by integrating feature maps from the beginning and the end of a network stage, which reduces computations by 20% with equivalent or even superior accuracy on the ImageNet dataset, and significantly outperforms state-of-the-art approaches in terms of AP50 on the MS COCO object detection dataset.

Focal Loss for Dense Object Detection

This paper proposes to address the extreme foreground-background class imbalance encountered during training of dense detectors by reshaping the standard cross entropy loss such that it down-weights the loss assigned to well-classified examples, and develops a novel Focal Loss, which focuses training on a sparse set of hard examples and prevents the vast number of easy negatives from overwhelming the detector during training.

Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks

This work introduces a Region Proposal Network (RPN) that shares full-image convolutional features with the detection network, thus enabling nearly cost-free region proposals and further merge RPN and Fast R-CNN into a single network by sharing their convolutionAL features.

FCOS: Fully Convolutional One-Stage Object Detection

For the first time, a much simpler and flexible detection framework achieving improved detection accuracy is demonstrated, and it is hoped that the proposed FCOS framework can serve as a simple and strong alternative for many other instance-level tasks.

Microsoft COCO: Common Objects in Context

We present a new dataset with the goal of advancing the state-of-the-art in object recognition by placing the question of object recognition in the context of the broader question of scene