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Yizhou Chen

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16 papers

2 author rows

JBHI Journal 2025 Journal Article

Multi-Task Collaborative Assisted Training Method for Grouping Fuzzy Categories Classification of Cervical Cancer Cells

Yizhou Chen
Huiyan Jiang
Wenbo Pang
Zhaoshuo Diao
Jing Yang

Cervical cancer is a malignant tumor that endangers women's life and health. While deep learning has enhanced the accuracy of cervical cell classification, there remain obstacles impeding further performance enhancement, including the similarities between different categories, variability between single cells and cell clusters, as well as the accuracy of annotations. To address these issues, a novel multi-task collaborative framework for cervical cell classification is proposed. Specifically, to solve the similarity between different categories, we propose a grouping cell contrast auxiliary branch, which divides cervical cells into different groups and utilizes supervised contrastive learning to learn representative feature between different categories. And we introduce a multi-level cell classification auxiliary branch that simultaneously performs 5-class, 3-class, and 2-class classification tasks, and explicitly constrains the inter-class relationship learning of cervical cells. Furthermore, to solve the variations within the same category of single cells and cell clusters, we propose an image reconstruction auxiliary branch, which encourages the model to learn more contextual features. Finally, to solve subjectivity and accuracy of annotations, we introduce a soft label distillation auxiliary branch, which constrains the consistency of probability distributions between the encoder and the momentum encoder. It is worth noting that these auxiliary branches only work during training and will not add additional computational consumption during inference. We validate on the HSJCC, DSCC and SIPaKMeD datasets. Compared to existing methods, our approach has achieved outstanding performance and effectively mitigates the issues raised, demonstrating its effectiveness in automated cervical cell classification.

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ICRA Conference 2024 Conference Paper

Air Bumper: A Collision Detection and Reaction Framework for Autonomous MAV Navigation

Ruoyu Wang 0032
Zixuan Guo
Yizhou Chen
Xinyi Wang 0007
Ben M. Chen

Autonomous navigation in unknown environments with obstacles remains challenging for micro aerial vehicles (MAVs) due to their limited onboard computing and sensing resources. Although various collision avoidance methods have been developed, it is still possible for drones to collide with unobserved obstacles due to unpredictable disturbances, sensor limitations, and control uncertainty. Instead of completely avoiding collisions, this article proposes Air Bumper, a collision detection and reaction framework, for fully autonomous flight in 3D environments to improve flight safety. Our framework only utilizes the onboard inertial measurement unit (IMU) to detect and estimate collisions. We further design a collision recovery control for rapid recovery and collision-aware mapping to integrate collision information into general LiDAR-based sensing and planning frameworks. Our simulation and experimental results show that the drone can rapidly detect, estimate, and recover from collisions with obstacles in 3D space and continue the flight smoothly with the help of the collision-aware map. In addition, we will open-source the implementation of Air Bumper on GitHub 1.

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IJCAI Conference 2024 Conference Paper

Dynamically Anchored Prompting for Task-Imbalanced Continual Learning

Chenxing Hong
Yan Jin
Zhiqi Kang
Yizhou Chen
Mengke Li
Yang Lu
Hanzi Wang

Existing continual learning literature relies heavily on a strong assumption that tasks arrive with a balanced data stream, which is often unrealistic in real-world applications. In this work, we explore task-imbalanced continual learning (TICL) scenarios where the distribution of task data is non-uniform across the whole learning process. We find that imbalanced tasks significantly challenge the capability of models to control the trade-off between stability and plasticity from the perspective of recent prompt-based continual learning methods. On top of the above finding, we propose Dynamically Anchored Prompting (DAP), a prompt-based method that only maintains a single general prompt to adapt to the shifts within a task stream dynamically. This general prompt is regularized in the prompt space with two specifically designed prompt anchors, called boosting anchor and stabilizing anchor, to balance stability and plasticity in TICL. Remarkably, DAP achieves this balance by only storing a prompt across the data stream, therefore offering a substantial advantage in rehearsal-free CL. Extensive experiments demonstrate that the proposed DAP results in 4. 5% to 15% absolute improvements over state-of-the-art methods on benchmarks under task-imbalanced settings. Our code is available at https: //github. com/chenxing6666/DAP.

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ICRA Conference 2024 Conference Paper

Sensor-based Multi-Robot Coverage Control with Spatial Separation in Unstructured Environments

Xinyi Wang 0007
Jiwen Xu
Chuanxiang Gao
Yizhou Chen
Jihan Zhang
Chenggang Wang
Yulong Ding
Ben M. Chen

Multi-robot systems have increasingly become instrumental in tackling coverage problems. However, the challenge of optimizing task efficiency without compromising task success still persists, particularly in expansive, unstructured scenarios with dense obstacles. This paper presents an innovative, decentralized Voronoi-based coverage control approach to reactively navigate these complexities while guaranteeing safety. This approach leverages the active sensing capabilities of multi-robot systems to supplement GIS (Geographic Information System), offering a more comprehensive and real-time understanding of environments like post-disaster. Based on point cloud data, which is inherently non-convex and unstructured, this method efficiently generates collision-free Voronoi regions using only local sensing information through spatial decomposition and spherical mirroring techniques. Then, deadlock-aware guided map integrated with a gradient-optimized, centroid Voronoi-based coverage control policy, is constructed to improve efficiency by avoiding exhaustive searches and local sensing pitfalls. The effectiveness of our algorithm has been validated through extensive numerical simulations in high-fidelity environments, demonstrating significant improvements in task success rate, coverage ratio, and task execution time compared with others.

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IROS Conference 2023 Conference Paper

ADMNet: Anti-Drone Real-Time Detection and Monitoring

Xunkuai Zhou
Guidong Yang
Yizhou Chen
Chuangxiang Gao
Benyun Zhao
Li Li 0008
Ben M. Chen

We propose a lightweight, effective, and efficient anti-drone network, namely ADMNet, for visually detecting and monitoring unfriendly drones with a constrained view field, flying against a complex environment. We merge an SPP module to the first head of YOLOv4 to improve accuracy and perform network compression to reduce inference latency and model size. To compensate for the accuracy loss caused by condensation, we propose an SPPS module and a ResNeck module for the neck of the network and implement an effective attention module for the backbone. Eventually, we present an accurate and compact ADMNet with barely 3. 9 MB, ensuring low computational cost and real-time detection. Our method achieves state-of-the-art performance on three challenging real-world datasets (Average Precision @0. 5IoU): Det-Fly 96. 2%, NPS-Drones 92. 0%, and TIBNet 89. 7%. The throughput is higher than the prior work, in addition to its superior performance. The comparative testing in real-world scenarios proves that our method exhibits strong reliability and generalization ability. Deploying the network on drone onboard edge-computing devices enables real-time detection and monitoring of flying drones, highlighting the portability and viability of the ADMNet.

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IROS Conference 2023 Conference Paper

An Interactive System for Multiple-Task Linear Temporal Logic Path Planning

Yizhou Chen
Xinyi Wang 0007
Zixuan Guo
Ruoyu Wang 0032
Xunkuai Zhou
Guidong Yang
Shupeng Lai
Ben M. Chen

Beyond programming robots to accomplish a single high-level task at a time, people also hope robots follow instructions and complete a series of tasks while meeting their requirements. This paper presents an interactive software system that consists of a multiple-task linear temporal logic (LTL) path planner and a human-machine interface (HMI). The HMI transforms human oral instructions into task commands that can be understood by the machine. The planner grows a rapid random exploring tree to search for solutions for multiple tasks. When switching tasks, the search tree is re-initialized and reconnected to utilize the information gathered during the exploration of the workspace. The feasibility of the improved planner is theoretically guaranteed, and profiling in simulation shows an acceleration in planning. An experiment with a quadcopter is conducted to show that the combination of the multiple-task LTL planner and the HMI results in a synergistic effect in real-world applications.

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AAAI Conference 2023 Conference Paper

Multi-Domain Generalized Graph Meta Learning

Mingkai Lin
Wenzhong Li
Ding Li
Yizhou Chen
Guohao Li
Sanglu Lu

Graph meta learning aims to learn historical knowledge from training graph neural networks (GNNs) models and adapt it to downstream learning tasks in a target graph, which has drawn increasing attention due to its ability of knowledge transfer and fast adaptation. While existing graph meta learning approaches assume the learning tasks are from the same graph domain but lack the solution for multi-domain adaptation. In this paper, we address the multi-domain generalized graph meta learning problem, which is challenging due to non-Euclidean data, inequivalent feature spaces, and heterogeneous distributions. To this end, we propose a novel solution called MD-Gram for multi-domain graph generalization. It introduces an empirical graph generalization method that uses empirical vectors to form a unified expression of non-Euclidean graph data. Then it proposes a multi-domain graphs transformation approach to transform the learning tasks from multiple source-domain graphs with inequivalent feature spaces into a common domain, where graph meta learning is conducted to learn generalized knowledge. It further adopts a domain-specific GNN enhancement method to learn a customized GNN model to achieve fast adaptation in the unseen target domain. Extensive experiments based on four real-world graph domain datasets show that the proposed method significantly outperforms the state-of-the-art in multi-domain graph meta learning tasks.

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IROS Conference 2023 Conference Paper

Multi-View Stereo with Learnable Cost Metric

Guidong Yang
Xunkuai Zhou
Chuanxiang Gao
Benyun Zhao
Jihan Zhang
Yizhou Chen
Xi Chen 0104
Ben M. Chen

In this paper, we present LCM-MVSNet, a novel multi-view stereo (MVS) network with learnable cost metric (LCM) for more accurate and complete depth estimation and dense point cloud reconstruction. To adapt to the scene variation and improve the reconstruction quality in non-Lambertian low-textured scenes, we propose LCM to adaptively aggregate multi-view matching similarity into the 3D cost volume by leveraging sparse points hints. The proposed LCM benefits the MVS approaches in four folds, including depth estimation enhancement, reconstruction quality improvement, memory footprint reduction, and computational burden alleviation, allowing the depth inference for high-resolution images to achieve more accurate and complete reconstruction. Moreover, we improve the depth estimation by enhancing the propagation of shallow features via a bottom-up path and strengthen the end-to-end supervision by adapting the focal loss to reduce ambiguity caused by sample imbalance. Extensive experiments on two benchmark datasets show that our network achieves state-of-the-art performance on the DTU dataset and exhibits strong generalization ability with a competitive performance on the Tanks and Temples benchmark. Furthermore, we deploy our LCM-MVSNet into the real-world application for large-scale 3D reconstruction based on multi-view aerial images collected by self-developed UAV, demonstrating the robustness and scalability of our method. More detailed results are available in the Appendix 1 1 shorturl. at/rBG28

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NeurIPS Conference 2023 Conference Paper

Recurrent Temporal Revision Graph Networks

Yizhou Chen
Anxiang Zeng
Qingtao Yu
Kerui Zhang
Cao Yuanpeng
Kangle Wu
Guangda Huzhang
Han Yu

Temporal graphs offer more accurate modeling of many real-world scenarios than static graphs. However, neighbor aggregation, a critical building block of graph networks, for temporal graphs, is currently straightforwardly extended from that of static graphs. It can be computationally expensive when involving all historical neighbors during such aggregation. In practice, typically only a subset of the most recent neighbors are involved. However, such subsampling leads to incomplete and biased neighbor information. To address this limitation, we propose a novel framework for temporal neighbor aggregation that uses the recurrent neural network with node-wise hidden states to integrate information from all historical neighbors for each node to acquire the complete neighbor information. We demonstrate the superior theoretical expressiveness of the proposed framework as well as its state-of-the-art performance in real-world applications. Notably, it achieves a significant +9. 4% improvement on averaged precision in a real-world Ecommerce dataset over existing methods on 2-layer models.

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AIJ Journal 2023 Journal Article

Recursive reasoning-based training-time adversarial machine learning

Yizhou Chen
Zhongxiang Dai
Haibin Yu
Bryan Kian Hsiang Low
Teck-Hua Ho

The training process of a machine learning (ML) model may be subject to adversarial attacks from an attacker who attempts to undermine the test performance of the ML model by perturbing the training minibatches, and thus needs to be protected by a defender. Such a problem setting is referred to as training-time adversarial ML. We formulate it as a two-player game and propose a principled Recursive Reasoning-based Training-Time adversarial ML (R2T2) framework to model this game. R2T2 models the reasoning process between the attacker and the defender and captures their bounded reasoning capabilities (due to bounded computational resources) through the recursive reasoning formalism. In particular, we associate a deeper level of recursive reasoning with the use of a higher-order gradient to derive the attack (defense) strategy, which naturally improves its performance while requiring greater computational resources. Interestingly, our R2T2 framework encompasses a variety of existing adversarial ML methods which correspond to attackers (defenders) with different recursive reasoning capabilities. We show how an R2T2 attacker (defender) can utilize our proposed nested projected gradient descent-based method to approximate the optimal attack (defense) strategy at an arbitrary level of reasoning. R2T2 can empirically achieve state-of-the-art attack and defense performances on benchmark image datasets.

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ICRA Conference 2023 Conference Paper

Sampling-based path planning under temporal logic constraints with real-time adaptation

Yizhou Chen
Ruoyu Wang 0032
Xinyi Wang 0007
Ben M. Chen

Replanning in temporal logic tasks is extremely difficult during the online execution of robots. This study introduces an effective path planner that computes solutions for temporal logic goals and instantly adapts to non-static and partially unknown environments. Given prior knowledge and a task specification, the planner first identifies an initial feasible solution by growing a sampling-based search tree. While carrying out the computed plan, the robot maintains a solution library to continuously enhance the unfinished part of the plan and store backup plans. The planner updates existing plans when meeting unexpected obstacles or recognizing flaws in prior knowledge. Upon a high-level path is obtained, a trajectory generator tracks the path by dividing it into segments of motion primitives. Our planner is integrated into an autonomous mobile robot system, further deployed on a multicopter with limited onboard processing power. In simulation and real-world experiments, our planner is demonstrated to swiftly and effectively adjust to environmental uncertainties.

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AAAI Conference 2022 Conference Paper

Cosine Model Watermarking against Ensemble Distillation

Laurent Charette
Lingyang Chu
Yizhou Chen
Jian Pei
Lanjun Wang
Yong Zhang

Many model watermarking methods have been developed to prevent valuable deployed commercial models from being stealthily stolen by model distillations. However, watermarks produced by most existing model watermarking methods can be easily evaded by ensemble distillation, because averaging the outputs of multiple ensembled models can significantly reduce or even erase the watermarks. In this paper, we focus on tackling the challenging task of defending against ensemble distillation. We propose a novel watermarking technique named CosWM to achieve outstanding model watermarking performance against ensemble distillation. CosWM is not only elegant in design, but also comes with desirable theoretical guarantees. Our extensive experiments on public data sets demonstrate the excellent performance of CosWM and its advantages over the state-of-the-art baselines.

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UAI Conference 2022 Conference Paper

Neural ensemble search via Bayesian sampling

Yao Shu
Yizhou Chen
Zhongxiang Dai
Bryan Kian Hsiang Low

Recently, neural architecture search (NAS) has been applied to automate the design of neural networks in real-world applications. A large number of algorithms have been developed to improve the search cost or the performance of the final selected architectures in NAS. Unfortunately, these NAS algorithms aim to select only one single well-performing architecture from their search spaces and thus have overlooked the capability of neural network ensemble (i. e. , an ensemble of neural networks with diverse architectures) in achieving improved performance over a single final selected architecture. To this end, we introduce a novel neural ensemble search algorithm, called neural ensemble search via Bayesian sampling (NESBS), to effectively and efficiently select well-performing neural network ensembles from a NAS search space. In our extensive experiments, NESBS algorithm is shown to be able to achieve improved performance over state-of-the-art NAS algorithms while incurring a comparable search cost, thus indicating the superior performance of our NESBS algorithm over these NAS algorithms in practice.

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UAI Conference 2022 Conference Paper

On provably robust meta-Bayesian optimization

Zhongxiang Dai
Yizhou Chen
Haibin Yu
Bryan Kian Hsiang Low
Patrick Jaillet

Bayesian optimization (BO) has become popular for sequential optimization of black-box functions. When BO is used to optimize a target function, we often have access to previous evaluations of potentially related functions. This begs the question as to whether we can leverage these previous experiences to accelerate the current BO task through meta-learning (meta-BO), while ensuring robustness against potentially harmful dissimilar tasks that could sabotage the convergence of BO. This paper introduces two scalable and provably robust meta-BO algorithms: robust meta-Gaussian process-upper confidence bound (RM-GP-UCB) and RM-GP-Thompson sampling (RM-GP-TS). We prove that both algorithms are asymptotically no-regret even when some or all previous tasks are dissimilar to the current task, and show that RM-GP-UCB enjoys a better theoretical robustness than RM-GP-TS. We also exploit the theoretical guarantees to optimize the weights assigned to individual previous tasks through regret minimization via online learning, which diminishes the impact of dissimilar tasks and hence further enhances the robustness. Empirical evaluations show that (a) RM-GP-UCB performs effectively and consistently across various applications, and (b) RM-GP-TS, despite being less robust than RM-GP-UCB both in theory and in practice, performs competitively in some scenarios with less dissimilar tasks and is more computationally efficient.

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ICML Conference 2020 Conference Paper

R2-B2: Recursive Reasoning-Based Bayesian Optimization for No-Regret Learning in Games

Zhongxiang Dai
Yizhou Chen
Bryan Kian Hsiang Low
Patrick Jaillet
Teck-Hua Ho

This paper presents a recursive reasoning formalism of Bayesian optimization (BO) to model the reasoning process in the interactions between boundedly rational, self-interested agents with unknown, complex, and costly-to-evaluate payoff functions in repeated games, which we call Recursive Reasoning-Based BO (R2-B2). Our R2-B2 algorithm is general in that it does not constrain the relationship among the payoff functions of different agents and can thus be applied to various types of games such as constant-sum, general-sum, and common-payoff games. We prove that by reasoning at level 2 or more and at one level higher than the other agents, our R2-B2 agent can achieve faster asymptotic convergence to no regret than that without utilizing recursive reasoning. We also propose a computationally cheaper variant of R2-B2 called R2-B2-Lite at the expense of a weaker convergence guarantee. The performance and generality of our R2-B2 algorithm are empirically demonstrated using synthetic games, adversarial machine learning, and multi-agent reinforcement learning.

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NeurIPS Conference 2019 Conference Paper

Implicit Posterior Variational Inference for Deep Gaussian Processes

Haibin Yu
Yizhou Chen
Bryan Kian Hsiang Low
Patrick Jaillet
Zhongxiang Dai

A multi-layer deep Gaussian process (DGP) model is a hierarchical composition of GP models with a greater expressive power. Exact DGP inference is intractable, which has motivated the recent development of deterministic and stochastic approximation methods. Unfortunately, the deterministic approximation methods yield a biased posterior belief while the stochastic one is computationally costly. This paper presents an implicit posterior variational inference (IPVI) framework for DGPs that can ideally recover an unbiased posterior belief and still preserve time efficiency. Inspired by generative adversarial networks, our IPVI framework achieves this by casting the DGP inference problem as a two-player game in which a Nash equilibrium, interestingly, coincides with an unbiased posterior belief. This consequently inspires us to devise a best-response dynamics algorithm to search for a Nash equilibrium (i. e. , an unbiased posterior belief). Empirical evaluation shows that IPVI outperforms the state-of-the-art approximation methods for DGPs.

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