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Peizhen Zhang

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2 papers

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AAAI Conference 2022 Conference Paper

LGD: Label-Guided Self-Distillation for Object Detection

Peizhen Zhang
Zijian Kang
Tong Yang
Xiangyu Zhang
Nanning Zheng
Jian Sun

In this paper, we propose the first self-distillation framework for general object detection, termed LGD (Label-Guided self-Distillation). Previous studies rely on a strong pretrained teacher to provide instructive knowledge that could be unavailable in real-world scenarios. Instead, we generate an instructive knowledge based only on student representations and regular labels. Our framework includes sparse labelappearance encoder, inter-object relation adaptater and intraobject knowledge mapper that jointly form an implicit teacher at training phase, dynamically dependent on labels and evolving student representations. They are trained end-to-end with detector and discarded in inference. Experimentally, LGD obtains decent results on various detectors, datasets, and extensive tasks like instance segmentation. For example in MS- COCO dataset, LGD improves RetinaNet with ResNet-50 under 2× single-scale training from 36. 2% to 39. 0% mAP (+ 2. 8%). It boosts much stronger detectors like FCOS with ResNeXt-101 DCN v2 under 2× multi-scale training from 46. 1% to 47. 9% (+ 1. 8%). Compared with a classical teacherbased method FGFI, LGD not only performs better without requiring pretrained teacher but also reduces 51% training cost beyond inherent student learning. Codes are available at https: //github. com/megvii-research/LGD.

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NeurIPS Conference 2021 Conference Paper

Instance-Conditional Knowledge Distillation for Object Detection

Zijian Kang
Peizhen Zhang
Xiangyu Zhang
Jian Sun
Nanning Zheng

Knowledge distillation has shown great success in classification, however, it is still challenging for detection. In a typical image for detection, representations from different locations may have different contributions to detection targets, making the distillation hard to balance. In this paper, we propose a conditional distillation framework to distill the desired knowledge, namely knowledge that is beneficial in terms of both classification and localization for every instance. The framework introduces a learnable conditional decoding module, which retrieves information given each target instance as query. Specifically, we encode the condition information as query and use the teacher's representations as key. The attention between query and key is used to measure the contribution of different features, guided by a localization-recognition-sensitive auxiliary task. Extensive experiments demonstrate the efficacy of our method: we observe impressive improvements under various settings. Notably, we boost RetinaNet with ResNet-50 backbone from $37. 4$ to $40. 7$ mAP ($+3. 3$) under $1\times$ schedule, that even surpasses the teacher ($40. 4$ mAP) with ResNet-101 backbone under $3\times$ schedule. Code has been released on https: //github. com/megvii-research/ICD.

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