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Chao Qi

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4 papers

2 author rows

IROS Conference 2025 Conference Paper

Towards Physically Realizable Adversarial Attacks in Embodied Vision Navigation

Meng Chen
Jiawei Tu
Chao Qi
Yonghao Dang
Feng Zhou
Wei Wei
Jianqin Yin

The significant advancements in embodied vision navigation have raised concerns about its susceptibility to adversarial attacks exploiting deep neural networks. Investigating the adversarial robustness of embodied vision navigation is crucial, especially given the threat of 3D physical attacks that could pose risks to human safety. However, existing attack methods for embodied vision navigation often lack physical feasibility due to challenges in transferring digital perturbations into the physical world. Moreover, current physical attacks for object detection struggle to achieve both multi-view effectiveness and visual naturalness in navigation scenarios. To address this, we propose a practical attack method for embodied navigation by attaching adversarial patches to objects, where both opacity and textures are learnable. Specifically, to ensure effectiveness across varying viewpoints, we employ a multi-view optimization strategy based on object-aware sampling, which optimizes the patch’s texture based on feedback from the vision-based perception model used in navigation. To make the patch inconspicuous to human observers, we introduce a two-stage opacity optimization mechanism, in which opacity is fine-tuned after texture optimization. Experimental results demonstrate that our adversarial patches decrease the navigation success rate by an average of 22. 39%, outperforming previous methods in practicality, effectiveness, and naturalness. Code is available at: github.com/chen37058/Physical-Attacks-in-Embodied-Nav.

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EAAI Journal 2024 Journal Article

A parallel and multi-scale probabilistic temporal convolutional neural networks for forecasting the key monitoring parameters of gas turbine

Changjun Liu
Xiaofang Wang
Xiaomo Jiang
Yongfeng Sui
Shiduo Cao
Chao Qi
Haitao Liu

As a crucial equipment in the power industry, gas turbines need effective condition monitoring techniques to maintain safe and reliable operations. Fast yet accurate long-term forecasting of the key monitoring parameters of gas turbine is vital for achieving the overall effective condition monitoring. This however poses challenges in terms of both efficacy and efficiency for conventional time series models like recurrent neural networks (RNN), since they run in a sequential manner. To this end, this paper introduces a novel parallel probabilistic time series prediction model. Particularly, the proposed model leverages the parallelism of temporal convolutional neural network (TCN) and exploits the power of latent space in the Bayesian framework. Besides, a multi-scale feature extraction strategy based on dense connections and a lagged observation strategy are presented to improve the model performance. In the comparative study against state-of-the art competitors on four classical system identification benchmarks, the proposed model significantly reduces the training time, while consistently achieving highly accurate predictions and providing appropriate uncertainty quantification. Thereafter, the proposed model is adopted to build a systematic workflow for the fast yet accurate long-term forecasting of the temperature of blade channel of gas turbine. The comprehensive results again highlight the superiority of the proposed model in terms of both the prediction quality and the training efficiency.

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ICRA Conference 2021 Conference Paper

Neighborhood Spatial Aggregation based Efficient Uncertainty Estimation for Point Cloud Semantic Segmentation

Chao Qi
Jianqin Yin
Huaping Liu 0001
Jun Liu 0007

Uncertainty estimation for point cloud semantic segmentation is to quantify the confidence degree for the predicted label of points, which is essential for decision-making tasks. This paper proposes a neighborhood spatial aggregation based method, NSA-MC dropout, to achieve efficient uncertainty estimation for point cloud semantic segmentation. Unlike the traditional uncertainty estimation method MC dropout de-pending on repeated inferences, our NSA-MC dropout achieves uncertainty estimation through one-time inference. Specifically, a space-dependent method is designed to sample the model many times by performing stochastic forward pass through the model just once, and it approximates the repeated inferences based sampling process in MC dropout. Besides, a neighborhood spatial aggregation module, called NSA, aggregates neighborhood probabilistic outputs for each point and works with space-dependent sampling to establish output distribution. Finally, we propose an uncertainty-aware framework NSA-MC dropout to capture the uncertainty of prediction results efficiently. Experimental results show that our method obtains comparable performance with MC dropout. More significantly, our NSA-MC dropout has little influence on the efficiency of semantic inference. It is much faster than MC dropout, and the inference time does not establish a coupling relation with the sampling times. Our code is available at https://github.com/chaoqi7/Uncertainty_Estimation_PCSS

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IJCAI Conference 2019 Conference Paper

Representation Learning-Assisted Click-Through Rate Prediction

Wentao Ouyang
Xiuwu Zhang
Shukui Ren
Chao Qi
Zhaojie Liu
Yanlong Du

Click-through rate (CTR) prediction is a critical task in online advertising systems. Most existing methods mainly model the feature-CTR relationship and suffer from the data sparsity issue. In this paper, we propose DeepMCP, which models other types of relationships in order to learn more informative and statistically reliable feature representations, and in consequence to improve the performance of CTR prediction. In particular, DeepMCP contains three parts: a matching subnet, a correlation subnet and a prediction subnet. These subnets model the user-ad, ad-ad and feature-CTR relationship respectively. When these subnets are jointly optimized under the supervision of the target labels, the learned feature representations have both good prediction powers and good representation abilities. Experiments on two large-scale datasets demonstrate that DeepMCP outperforms several state-of-the-art models for CTR prediction.

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