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Cuihua Li

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3 papers

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AAAI Conference 2022 Conference Paper

Uncertainty-Driven Dehazing Network

Ming Hong
Jianzhuang Liu
Cuihua Li
Yanyun Qu

Deep learning has made remarkable achievements for single image haze removal. However, existing deep dehazing models only give deterministic results without discussing their uncertainty. There exist two types of uncertainty in the dehazing models: aleatoric uncertainty that comes from noise inherent in the observations and epistemic uncertainty that accounts for uncertainty in the model. In this paper, we propose a novel uncertainty-driven dehazing network (UDN) that improves the dehazing results by exploiting the relationship between the uncertain and confident representations. We first introduce an Uncertainty Estimation Block (UEB) to predict the aleatoric and epistemic uncertainty together. Then, we propose an Uncertainty-aware Feature Modulation (UFM) block to adaptively enhance the learned features. UFM predicts a convolution kernel and channel-wise modulation coefficients conditioned on the uncertainty weighted representation. Moreover, we develop an uncertainty-driven self-distillation loss to improve the uncertain representation by transferring the knowledge from the confident one. Extensive experimental results on synthetic datasets and real-world images show that UDN achieves significant quantitative and qualitative improvements, outperforming state-of-the-arts.

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AAAI Conference 2021 Conference Paper

Weakly Supervised Semantic Segmentation for Large-Scale Point Cloud

Yachao Zhang
Zonghao Li
Yuan Xie
Yanyun Qu
Cuihua Li
Tao Mei

Existing methods for large-scale point cloud semantic segmentation require expensive, tedious and error-prone manual point-wise annotations. Intuitively, weakly supervised training is a direct solution to reduce the cost of labeling. However, for weakly supervised large-scale point cloud semantic segmentation, too few annotations will inevitably lead to ineffective learning of network. We propose an effective weakly supervised method containing two components to solve the above problem. Firstly, we construct a pretext task, i. e. , point cloud colorization, with a self-supervised learning to transfer the learned prior knowledge from a large amount of unlabeled point cloud to a weakly supervised network. In this way, the representation capability of the weakly supervised network can be improved by the guidance from a heterogeneous task. Besides, to generate pseudo label for unlabeled data, a sparse label propagation mechanism is proposed with the help of generated class prototypes, which is used to measure the classification confidence of unlabeled point. Our method is evaluated on large-scale point cloud datasets with different scenarios including indoor and outdoor. The experimental results show the large gain against existing weakly supervised methods and comparable results to fully supervised methods.

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AAAI Conference 2020 Conference Paper

Patch Proposal Network for Fast Semantic Segmentation of High-Resolution Images

Tong Wu
Zhenzhen Lei
Bingqian Lin
Cuihua Li
Yanyun Qu
Yuan Xie

Despite recent progress on the segmentation of highresolution images, there exist an unsolved problem, i. e. , the trade-off among the segmentation accuracy, memory resources and inference speed. So far, GLNet is introduced for high or ultra-resolution image segmentation, which has reduced the computational memory of the segmentation network. However, it ignores the importances of different cropped patches, and treats tiled patches equally for fusion with the whole image, resulting in high computational cost. To solve this problem, we introduce a patch proposal network (PPN) in this paper, which adaptively distinguishes the critical patches from the trivial ones to fuse with the whole image for reﬁning segmentation. PPN is a classiﬁcation network which alleviates network training burden and improves segmentation accuracy. We further embed PPN in a globallocal segmentation network, instructing global branch and re- ﬁnement branch to work collaboratively. We implement our method on four image datasets: DeepGlobe, ISIC, CRAG and Cityscapes, the ﬁrst two are ultra-resolution image datasets and the last two are high-resolution image datasets. The experimental results show that our method achieves almost the best segmentation performance compared with the state-ofthe-art segmentation methods and the inference speed is 12. 9 fps on DeepGlobe and 10 fps on ISIC. Moreover, we embed PPN with the general semantic segmentation network and the experimental results on Cityscapes which contains more object classes demonstrate the generalization ability on general semantic segmentation.

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