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Zengmao Wang

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9 papers

2 author rows

IROS Conference 2025 Conference Paper

Learning to Exploit Leg Odometry Enables Terrain-Aware Quadrupedal Locomotion

Yong Zhou
Jiawei Jiang
Bo Du
Zengmao Wang

The geometry of terrain is crucial for developing terrain-aware locomotion policies. Recent advancements in quadrupedal locomotion based on learning rely on depth information obtained from LiDARs and depth cameras. Despite the capabilities of these locomotion policies on terrains, they pose challenges in processing high-dimensional data in real time with onboard hardware. In this study, we develop a lightweight framework that utilizes only the intrinsic sensors of a quadrupedal robot to facilitate terrain-aware locomotion. We introduce a learning-based leg odometry, integrated with a locomotion policy trained through reinforcement learning. Utilizing blind localization from leg odometry alongside a pre-constructed height map enables the robot to navigate steps and stairs without incident. We assess the efficacy of our framework through simulations, where our results indicate that the robot achieves up to a 17% improvement in successful traversal rates and requires fewer point samples. By compensating for slippage during locomotion, our learning-based leg odometry surpasses traditional inertialleg odometry. Lastly, we validate the practical applicability of our models on a real robot, confirming their effectiveness in real-world settings.

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ICML Conference 2025 Conference Paper

When Data-Free Knowledge Distillation Meets Non-Transferable Teacher: Escaping Out-of-Distribution Trap is All You Need

Ziming Hong
Runnan Chen
Zengmao Wang
Bo Han 0003
Bo Du 0001
Tongliang Liu

Data-free knowledge distillation (DFKD) transfers knowledge from a teacher to a student without access the real in-distribution (ID) data. Its common solution is to use a generator to synthesize fake data and use them as a substitute for real ID data. However, existing works typically assume teachers are trustworthy, leaving the robustness and security of DFKD from untrusted teachers largely unexplored. In this work, we conduct the first investigation into distilling non-transferable learning (NTL) teachers using DFKD, where the transferability from an ID domain to an out-of-distribution (OOD) domain is prohibited. We find that NTL teachers fool DFKD through divert the generator’s attention from the useful ID knowledge to the misleading OOD knowledge. This hinders ID knowledge transfer but prioritizes OOD knowledge transfer. To mitigate this issue, we propose Adversarial Trap Escaping (ATEsc) to benefit DFKD by identifying and filtering out OOD-like synthetic samples. Specifically, inspired by the evidence that NTL teachers show stronger adversarial robustness on OOD samples than ID samples, we split synthetic samples into two groups according to their robustness. The fragile group is treated as ID-like data and used for normal knowledge distillation, while the robust group is seen as OOD-like data and utilized for forgetting OOD knowledge. Extensive experiments demonstrate the effectiveness of ATEsc for improving DFKD against NTL teachers.

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NeurIPS Conference 2024 Conference Paper

Can Language Models Perform Robust Reasoning in Chain-of-thought Prompting with Noisy Rationales?

Zhanke Zhou
Rong Tao
Jianing Zhu
Yiwen Luo
Zengmao Wang
Bo Han

This paper investigates an under-explored challenge in large language models (LLMs): chain-of-thought prompting with noisy rationales, which include irrelevant or inaccurate reasoning thoughts within examples used for in-context learning. We construct NoRa dataset that is tailored to evaluate the robustness of reasoning in the presence of noisy rationales. Our findings on NoRa dataset reveal a prevalent vulnerability to such noise among current LLMs, with existing robust methods like self-correction and self-consistency showing limited efficacy. Notably, compared to prompting with clean rationales, base LLM drops by 1. 4%-19. 8% in accuracy with irrelevant thoughts and more drastically by 2. 2%-40. 4% with inaccurate thoughts. Addressing this challenge necessitates external supervision that should be accessible in practice. Here, we propose the method of contrastive denoising with noisy chain-of-thought (CD-CoT). It enhances LLMs' denoising-reasoning capabilities by contrasting noisy rationales with only one clean rationale, which can be the minimal requirement for denoising-purpose prompting. This method follows a principle of exploration and exploitation: (1) rephrasing and selecting rationales in the input space to achieve explicit denoising and (2) exploring diverse reasoning paths and voting on answers in the output space. Empirically, CD-CoT demonstrates an average improvement of 17. 8% in accuracy over the base model and shows significantly stronger denoising capabilities than baseline methods. The source code is publicly available at: https: //github. com/tmlr-group/NoisyRationales.

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AAAI Conference 2024 Conference Paper

Cycle Self-Refinement for Multi-Source Domain Adaptation

Chaoyang Zhou
Zengmao Wang
Bo Du
Yong Luo

Multi-source domain adaptation (MSDA) aims to transfer knowledge from multiple source domains to the unlabeled target domain. In this paper, we propose a cycle self-refinement domain adaptation method, which progressively attempts to learn the dominant transferable knowledge in each source domain in a cycle manner. Specifically, several source-specific networks and a domain-ensemble network are adopted in the proposed method. The source-specific networks are adopted to provide the dominant transferable knowledge in each source domain for instance-level ensemble on predictions of the samples in target domain. Then these samples with high-confidence ensemble predictions are adopted to refine the domain-ensemble network. Meanwhile, to guide each source-specific network to learn more dominant transferable knowledge, we force the features of the target domain from the domain-ensemble network and the features of each source domain from the corresponding source-specific network to be aligned with their predictions from the corresponding networks. Thus the adaptation ability of source-specific networks and the domain-ensemble network can be improved progressively. Extensive experiments on Office-31, Office-Home and DomainNet show that the proposed method outperforms the state-of-the-art methods for most tasks.

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IJCAI Conference 2024 Conference Paper

LeMeViT: Efficient Vision Transformer with Learnable Meta Tokens for Remote Sensing Image Interpretation

Wentao Jiang
Jing Zhang
Di Wang
Qiming Zhang
Zengmao Wang
Bo Du

Due to spatial redundancy in remote sensing images, sparse tokens containing rich information are usually involved in self-attention (SA) to reduce the overall token numbers within the calculation, avoiding the high computational cost issue in Vision Transformers. However, such methods usually obtain sparse tokens by hand-crafted or parallel-unfriendly designs, posing a challenge to reach a better balance between efficiency and performance. Different from them, this paper proposes to use learnable meta tokens to formulate sparse tokens, which effectively learn key information meanwhile improving the inference speed. Technically, the meta tokens are first initialized from image tokens via cross-attention. Then, we propose Dual Cross-Attention (DCA) to promote information exchange between image tokens and meta tokens, where they serve as query and key (value) tokens alternatively in a dual-branch structure, significantly reducing the computational complexity compared to self-attention. By employing DCA in the early stages with dense visual tokens, we obtain the hierarchical architecture LeMeViT with various sizes. Experimental results in classification and dense prediction tasks show that LeMeViT has a significant 1. 7 × speedup, fewer parameters, and competitive performance compared to the baseline models, and achieves a better trade-off between efficiency and performance. The code is released at https: //github. com/ViTAE-Transformer/LeMeViT.

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NeurIPS Conference 2024 Conference Paper

What If the Input is Expanded in OOD Detection?

Boxuan Zhang
Jianing Zhu
Zengmao Wang
Tongliang Liu
Bo Du
Bo Han

Out-of-distribution (OOD) detection aims to identify OOD inputs from unknown classes, which is important for the reliable deployment of machine learning models in the open world. Various scoring functions are proposed to distinguish it from in-distribution (ID) data. However, existing methods generally focus on excavating the discriminative information from a single input, which implicitly limits its representation dimension. In this work, we introduce a novel perspective, i. e. , employing different common corruptions on the input space, to expand that. We reveal an interesting phenomenon termed confidence mutation, where the confidence of OOD data can decrease significantly under the corruptions, while the ID data shows a higher confidence expectation considering the resistance of semantic features. Based on that, we formalize a new scoring method, namely, Confidence aVerage (CoVer), which can capture the dynamic differences by simply averaging the scores obtained from different corrupted inputs and the original ones, making the OOD and ID distributions more separable in detection tasks. Extensive experiments and analyses have been conducted to understand and verify the effectiveness of CoVer.

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IJCAI Conference 2022 Conference Paper

Self-paced Supervision for Multi-source Domain Adaptation

Zengmao Wang
Chaoyang Zhou
Bo Du
Fengxiang He

Multi-source domain adaptation has attracted great attention in machine learning community. Most of these methods focus on weighting the predictions produced by the adaptation networks of different domains. Thus the domain shifts between certain of domains and target domain are not effectively relieved, resulting in that these domains are not fully exploited and even may have a negative influence on multi-source domain adaptation task. To address such challenge, we propose a multi-source domain adaptation method to gradually improve the adaptation ability of each source domain by producing more high-confident pseudo-labels with self-paced learning for conditional distribution alignment. The proposed method first trains several separate domain branch networks with single domains and an ensemble branch network with all domains. Then we obtain some high-confident pseudo-labels with the branch networks and learn the branch specific pseudo-labels with self-paced learning. Each branch network reduces the domain gap by aligning the conditional distribution with its branch specific pseudo-labels and the pseudo-labels provided by all branch networks. Experiments on Office31, Office-Home and DomainNet show that the proposed method outperforms the state-of-the-art methods.

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IJCAI Conference 2018 Conference Paper

Matrix completion with Preference Ranking for Top-N Recommendation

Zengmao Wang
Yuhong Guo
Bo Du

Matrix completion has become a popular method for top-N recommendation due to the low rank nature of sparse rating matrices. However, many existing methods produce top-N recommendations by recovering a user-item matrix solely based on a low rank function or its relaxations, while ignoring other important intrinsic characteristics of the top-N recommendation tasks such as preference ranking over the items. In this paper, we propose a novel matrix completion method that integrates the low rank and preference ranking characteristics of recommendation matrix under a self-recovery model for top-N recommendation. The proposed method is formulated as a joint minimization problem and solved using an ADMM algorithm. We conduct experiments on E-commerce datasets. The experimental results show the proposed approach outperforms several state-of-the-art methods.

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IJCAI Conference 2017 Conference Paper

On Gleaning Knowledge from Multiple Domains for Active Learning

Zengmao Wang
Bo Du
Lefei Zhang
Liangpei Zhang
Ruimin Hu
Dacheng Tao

How can a doctor diagnose new diseases with little historical knowledge, which are emerging over time? Active learning is a promising way to address the problem by querying the most informative samples. Since the diagnosed cases for new disease are very limited, gleaning knowledge from other domains (classical prescriptions) to prevent the bias of active leaning would be vital for accurate diagnosis. In this paper, a framework that attempts to glean knowledge from multiple domains for active learning by querying the most uncertain and representative samples from the target domain and calculating the importance weights for re-weighting the source data in a single unified formulation is proposed. The weights are optimized by both a supervised classifier and distribution matching between the source domain and target domain with maximum mean discrepancy. Besides, a multiple domains active learning method is designed based on the proposed framework as an example. The proposed method is verified with newsgroups and handwritten digits data recognition tasks, where it outperforms the state-of-the-art methods.

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