Author name cluster

Chenshu Chen

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

2 papers

1 author row

AAAI Conference 2023 Conference Paper

MaskBooster: End-to-End Self-Training for Sparsely Supervised Instance Segmentation

Shida Zheng
Chenshu Chen
Xi Yang
Wenming Tan

The present paper introduces sparsely supervised instance segmentation, with the datasets being fully annotated bounding boxes and sparsely annotated masks. A direct solution to this task is self-training, which is not fully explored for instance segmentation yet. In this paper, we propose MaskBooster for sparsely supervised instance segmentation (SpSIS) with comprehensive usage of pseudo masks. MaskBooster is featured with (1) dynamic and progressive pseudo masks from an online updating teacher model, (2) refining binary pseudo masks with the help of bounding box prior, (3) learning inter-class prediction distribution via knowledge distillation for soft pseudo masks. As an end-to-end and universal self-training framework, MaskBooster can empower fully supervised algorithms and boost their segmentation performance on SpSIS. Abundant experiments are conducted on COCO and BDD100K datasets and validate the effectiveness of MaskBooster. Specifically, on different COCO protocols and BDD100K, we surpass sparsely supervised baseline by a large margin for both Mask RCNN and ShapeProp. MaskBooster on SpSIS also outperforms weakly and semi-supervised instance segmentation state-of-the-art on the datasets with similar annotation budgets.

PDF Details DOI

AAAI Conference 2022 Conference Paper

Dual Decoupling Training for Semi-supervised Object Detection with Noise-Bypass Head

Shida Zheng
Chenshu Chen
Xiaowei Cai
Tingqun Ye
Wenming Tan

Pseudo bounding boxes from the self-training paradigm are inevitably noisy for semi-supervised object detection. To cope with that, a dual decoupling training framework is proposed in the present study, i. e. clean and noisy data decoupling, and classification and localization task decoupling. In the first decoupling, two-level thresholds are used to categorize pseudo boxes into three groups, i. e. clean backgrounds, noisy foregrounds and clean foregrounds. With a specially designed noise-bypass head focusing on noisy data, backbone networks can extract coarse but diverse information; and meanwhile, an original head learns from clean samples for more precise predictions. In the second decoupling, we take advantage of the two-head structure for better evaluation of localization quality, thus the category label and location of a pseudo box can remain independent of each other during training. The approach of two-level thresholds is also applied to group pseudo boxes into three sections of different location accuracy. We outperform existing works by a large margin on VOC datasets, reaching 54. 8 mAP (+1. 8), and even up to 55. 9 mAP (+1. 5) by leveraging MS-COCO train2017 as extra unlabeled data. On MS-COCO benchmark, our method also achieves about 1. 0 mAP improvements averaging across protocols compared with the prior state-of-the-art.

PDF Details