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Zhen Zhao

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14 papers

2 author rows

EAAI Journal 2025 Journal Article

Cross-domain fault diagnosis of marine diesel engines based on stepwise diffusion and iterative bidirectional optimization

Zhen Zhao
Ziru Jin
Xin Xin
Yutong Fu
Xiaotong Huang
Liang Li
Hongyan Qin
Chong Wei

Cross-domain fault diagnosis of marine diesel engines presents significant challenges due to variations in data distribution and the limited availability of labeled fault samples under different operating conditions. To address this, an unsupervised domain-adaptive diagnostic framework is proposed, integrating stepwise diffusion and iterative bidirectional optimization to enhance fault identification. First, the quadratic axial attention transformer introduces a fourth weight in the axial computation to effectively capture the long-range spatio-temporal correlations in the time–frequency representations and strengthen the cross-axis contextual dependence. Next, the domain stepwise diffusion bridge utilizes Markov transform to gradually refine the significant distributional differences across domains into continuous sub-distributions, ensuring a smoother adaptation process. Finally, an iterative bidirectional optimization strategy is proposed to dynamically coordinate the interaction between stepwise diffusion and fault classification, where two complementary learning directions are alternately executed to preserve the semantic integrity of features. Experimental validation on a self-constructed dataset covering multiple operating conditions demonstrates the effectiveness of the proposed approach, achieving 93. 80 % average accuracy, 93. 75 % precision, and 93. 45 % recall. This approach not only breaks through the limitations of existing domain alignment methods and provides a brand new solution for cross-domain fault diagnosis, but also provides a wide range of implications for future research and applications in this field. The code and model are available at: https: //github. com/lazyJzr/UDAtask.

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NeurIPS Conference 2025 Conference Paper

FreqPolicy: Efficient Flow-based Visuomotor Policy via Frequency Consistency

Yifei Su
Ning Liu
Dong Chen
Zhen Zhao
Kun Wu
Meng Li
Zhiyuan Xu
Zhengping Che

Generative modeling-based visuomotor policies have been widely adopted in robotic manipulation, attributed to their ability to model multimodal action distributions. However, the high inference cost of multi-step sampling limits its applicability in real-time robotic systems. Existing approaches accelerate sampling in generative modeling-based visuomotor policies by adapting techniques originally developed to speed up image generation. However, a major distinction exists: image generation typically produces independent samples without temporal dependencies, while robotic manipulation requires generating action trajectories with continuity and temporal coherence. To this end, we propose FreqPolicy, a novel approach that first imposes frequency consistency constraints on flow-based visuomotor policies. Our work enables the action model to capture temporal structure effectively while supporting efficient, high-quality one-step action generation. Concretely, we introduce a frequency consistency constraint objective that enforces alignment of frequency-domain action features across different timesteps along the flow, thereby promoting convergence of one-step action generation toward the target distribution. In addition, we design an adaptive consistency loss to capture structural temporal variations inherent in robotic manipulation tasks. We assess FreqPolicy on $53$ tasks across $3$ simulation benchmarks, proving its superiority over existing one-step action generators. We further integrate FreqPolicy into the vision-language-action (VLA) model and achieve acceleration without performance degradation on $40$ tasks of Libero. Besides, we show efficiency and effectiveness in real-world robotic scenarios with an inference frequency of $93. 5$ Hz.

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ICRA Conference 2025 Conference Paper

Learning From Imperfect Demonstrations With Self-Supervision for Robotic Manipulation

Kun Wu 0001
Ning Liu 0007
Zhen Zhao
Di Qiu
Jinming Li
Zhengping Che
Zhiyuan Xu
Jian Tang 0008

Improving data utilization, especially for imperfect data from task failures, is crucial for robotic manipulation due to the challenging, time-consuming, and expensive data collection process in the real world. Current imitation learning (IL) typically discards imperfect data, focusing solely on successful expert data. While reinforcement learning (RL) can learn from explorations and failures, the sim2real gap and its reliance on dense reward and online exploration make it difficult to apply effectively in real-world scenarios. In this work, we aim to conquer the challenge of leveraging imperfect data without the need for reward information to improve the model performance for robotic manipulation in an offline manner. Specifically, we introduce a Self-Supervised Data Filtering framework (SSDF) that combines expert and imperfect data to compute quality scores for failed trajectory segments. High-quality segments from the failed data are used to expand the training dataset. Then, the enhanced dataset can be used with any downstream policy learning method for robotic manipulation tasks. Extensive experiments on the ManiSkill2 benchmark built on the high-fidelity Sapien simulator and real-world robotic manipulation tasks using the Franka robot arm demonstrated that the SSDF can accurately expand the training dataset with high-quality imperfect data and improve the success rates for all robotic manipulation tasks.

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ICLR Conference 2025 Conference Paper

TimeKAN: KAN-based Frequency Decomposition Learning Architecture for Long-term Time Series Forecasting

Songtao Huang
Zhen Zhao
Can Li 0014
Lei Bai 0001

Real-world time series often have multiple frequency components that are intertwined with each other, making accurate time series forecasting challenging. Decomposing the mixed frequency components into multiple single frequency components is a natural choice. However, the information density of patterns varies across different frequencies, and employing a uniform modeling approach for different frequency components can lead to inaccurate characterization. To address this challenges, inspired by the flexibility of the recent Kolmogorov-Arnold Network (KAN), we propose a KAN-based Frequency Decomposition Learning architecture (TimeKAN) to address the complex forecasting challenges caused by multiple frequency mixtures. Specifically, TimeKAN mainly consists of three components: Cascaded Frequency Decomposition (CFD) blocks, Multi-order KAN Representation Learning (M-KAN) blocks and Frequency Mixing blocks. CFD blocks adopt a bottom-up cascading approach to obtain series representations for each frequency band. Benefiting from the high flexibility of KAN, we design a novel M-KAN block to learn and represent specific temporal patterns within each frequency band. Finally, Frequency Mixing blocks is used to recombine the frequency bands into the original format. Extensive experimental results across multiple real-world time series datasets demonstrate that TimeKAN achieves state-of-the-art performance as an extremely lightweight architecture. Code is available at https://github.com/huangst21/TimeKAN.

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NeurIPS Conference 2024 Conference Paper

Depth Anything V2

Lihe Yang
Bingyi Kang
Zilong Huang
Zhen Zhao
Xiaogang Xu
Jiashi Feng
Hengshuang Zhao

This work presents Depth Anything V2. Without pursuing fancy techniques, we aim to reveal crucial findings to pave the way towards building a powerful monocular depth estimation model. Notably, compared with V1, this version produces much finer and more robust depth predictions through three key practices: 1) replacing all labeled real images with synthetic images, 2) scaling up the capacity of our teacher model, and 3) teaching student models via the bridge of large-scale pseudo-labeled real images. Compared with the latest models built on Stable Diffusion, our models are significantly more efficient (more than 10x faster) and more accurate. We offer models of different scales (ranging from 25M to 1. 3B params) to support extensive scenarios. Benefiting from their strong generalization capability, we fine-tune them with metric depth labels to obtain our metric depth models. In addition to our models, considering the limited diversity and frequent noise in current test sets, we construct a versatile evaluation benchmark with sparse depth annotations to facilitate future research. Models are available at https: //github. com/DepthAnything/Depth-Anything-V2.

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EAAI Journal 2024 Journal Article

Discriminative feature learning based on multi-view attention network with diffusion joint loss for speech emotion recognition

Yang Liu
Xin Chen
Yuan Song
Yarong Li
Shengbei Wang
Weitao Yuan
Yongwei Li
Zhen Zhao

In speech emotion recognition, existing models often struggle to accurately classify emotions with high similarity. In this paper, we propose a novel architecture that integrates a multi-view attention network (MVAN) and diffusion joint loss to alleviate confusion by placing a stronger focus on emotions that are challenging to classify accurately. First, we use logarithmic Mel-spectrograms (log-Mels), deltas, and delta-deltas of log-Mels as three-dimensional features to minimize external interference. Then, we design the MVAN to extract effective multi-time scale emotion features, where the channel and spatial attention are used to selectively localize the regions in the input features related to the target emotion. A Multi-time view bidirectional long and short-term memory network is used to extract the shallow edge features and deep semantic features, and multi-scale self-attention fuses these features through cross-scale attention fusion to obtain multi-time scale emotion features. Finally, a diffusion joint loss strategy is introduced to distinguish the emotional embeddings with high similarity by the generated complex emotion triplets in a diffusing fashion. We evaluated our proposed method on the Interactive Emotional Mood Binary Motion Capture (IEMOCAP), Chinese Academy of Sciences Automation Institute of Automation (CASIA), and Berlin German Emotion Speech Bank (EMODB) corpus. The results show significant improvements over existing methods, achieving 86. 87% WA, 86. 60% UA, and 86. 82% WF1 on IEMOCAP; 70. 74% WA, 70. 74% UA, and 70. 25% WF1 on CASIA; and 93. 65% WA, 91. 13% UA, and 92. 26% WF1 on EMODB. These results confirm the superiority of our method. Our code and model are available at https: //github. com/Littleznnz/MVAN-DiffSEG.

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NeurIPS Conference 2024 Conference Paper

Harmonizing Visual Text Comprehension and Generation

Zhen Zhao
Jingqun Tang
Binghong Wu
Chunhui Lin
Shu Wei
Hao Liu
Xin Tan
Zhizhong Zhang

In this work, we present TextHarmony, a unified and versatile multimodal generative model proficient in comprehending and generating visual text. Simultaneously generating images and texts typically results in performance degradation due to the inherent inconsistency between vision and language modalities. To overcome this challenge, existing approaches resort to modality-specific data for supervised fine-tuning, necessitating distinct model instances. We propose Slide-LoRA, which dynamically aggregates modality-specific and modality-agnostic LoRA experts, partially decoupling the multimodal generation space. Slide-LoRA harmonizes the generation of vision and language within a singular model instance, thereby facilitating a more unified generative process. Additionally, we develop a high-quality image caption dataset, DetailedTextCaps-100K, synthesized with a sophisticated closed-source MLLM to enhance visual text generation capabilities further. Comprehensive experiments across various benchmarks demonstrate the effectiveness of the proposed approach. Empowered by Slide-LoRA, TextHarmony achieves comparable performance to modality-specific fine-tuning results with only a 2% increase in parameters and shows an average improvement of 2. 5% in visual text comprehension tasks and 4. 0% in visual text generation tasks. Our work delineates the viability of an integrated approach to multimodal generation within the visual text domain, setting a foundation for subsequent inquiries. Code is available at https: //github. com/bytedance/TextHarmony.

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AAAI Conference 2024 Conference Paper

Roll with the Punches: Expansion and Shrinkage of Soft Label Selection for Semi-supervised Fine-Grained Learning

Yue Duan
Zhen Zhao
Lei Qi
Luping Zhou
Lei Wang
Yinghuan Shi

While semi-supervised learning (SSL) has yielded promising results, the more realistic SSL scenario remains to be explored, in which the unlabeled data exhibits extremely high recognition difficulty, e.g., fine-grained visual classification in the context of SSL (SS-FGVC). The increased recognition difficulty on fine-grained unlabeled data spells disaster for pseudo-labeling accuracy, resulting in poor performance of the SSL model. To tackle this challenge, we propose Soft Label Selection with Confidence-Aware Clustering based on Class Transition Tracking (SoC) by reconstructing the pseudo-label selection process by jointly optimizing Expansion Objective and Shrinkage Objective, which is based on a soft label manner. Respectively, the former objective encourages soft labels to absorb more candidate classes to ensure the attendance of ground-truth class, while the latter encourages soft labels to reject more noisy classes, which is theoretically proved to be equivalent to entropy minimization. In comparisons with various state-of-the-art methods, our approach demonstrates its superior performance in SS-FGVC. Checkpoints and source code are available at https://github.com/NJUyued/SoC4SS-FGVC.

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ICRA Conference 2024 Conference Paper

SM 3: Self-supervised Multi-task Modeling with Multi-view 2D Images for Articulated Objects

Haowen Wang 0001
Zhen Zhao
Zhao Jin
Zhengping Che
Liang Qiao
Yakun Huang
Zhipeng Fan
Xiuquan Qiao

Reconstructing real-world objects and estimating their movable joint structures are pivotal technologies within the field of robotics. Previous research has predominantly focused on supervised approaches, relying on annotated datasets to model articulated objects within limited categories. However, these approaches fall short of effectively addressing the diversity present in the real world. To tackle this issue, we propose a self-supervised interaction perception method, referred to as SM 3, which leverages multi-view RGB images captured before and after interaction to model articulated objects, identify the movable parts, and infer the parameters of their rotating joints. By constructing 3D geometries and textures from the captured 2D images, SM 3 achieves integrated optimization of movable part and joint parameters during the reconstruction process, obviating the need for annotations. Furthermore, we introduce the MMArt dataset, an extension of PartNet-Mobility, encompassing multi-view and multi-modal data of articulated objects spanning diverse categories. Evaluations demonstrate that SM 3 surpasses existing benchmarks across various categories and objects, and its adaptability in real-world scenarios has been thoroughly validated.

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YNIMG Journal 2024 Journal Article

Voxel-based texture similarity networks reveal individual variability and correlate with biological ontologies

Liyuan Lin
Zhongyu Chang
Yu Zhang
Kaizhong Xue
Yingying Xie
Luli Wei
Xin Li
Zhen Zhao

The human brain is organized as a complex, hierarchical network. However, the structural covariance patterns among brain regions and the underlying biological substrates of such covariance networks remain to be clarified. The present study proposed a novel individualized structural covariance network termed voxel-based texture similarity networks (vTSNs) based on 76 refined voxel-based textural features derived from structural magnetic resonance images. Validated in three independent longitudinal healthy cohorts (40, 23, and 60 healthy participants, respectively) with two common brain atlases, we found that the vTSN could robustly resolve inter-subject variability with high test-retest reliability. In contrast to the regional-based texture similarity networks (rTSNs) that calculate radiomic features based on region-of-interest information, vTSNs had higher inter- and intra-subject variability ratios and test-retest reliability in connectivity strength and network topological properties. Moreover, the Spearman correlation indicated a stronger association of the gene expression similarity network (GESN) with vTSNs than with rTSNs (vTSN: r = 0.600, rTSN: r = 0.433, z = 39.784, P < 0.001). Hierarchical clustering identified 3 vTSN subnets with differential association patterns with 13 coexpression modules, 16 neurotransmitters, 7 electrophysiology, 4 metabolism, and 2 large-scale structural and 4 functional organization maps. Moreover, these subnets had unique biological hierarchical organization from the subcortex-limbic system to the ventral neocortex and then to the dorsal neocortex. Based on 424 unrelated, qualified healthy subjects from the Human Connectome Project, we found that vTSNs could sensitively represent sex differences, especially for connections in the subcortex-limbic system and between the subcortex-limbic system and the ventral neocortex. Moreover, a multivariate variance component model revealed that vTSNs could explain a significant proportion of inter-subject behavioral variance in cognition (80.0 %) and motor functions (63.4 %). Finally, using 494 healthy adults (aged 19-80 years old) from the Southwest University Adult Lifespan Dataset, the Spearman correlation identified a significant association between aging and vTSN strength, especially within the subcortex-limbic system and between the subcortex-limbic system and the dorsal neocortex. In summary, our proposed vTSN is robust in uncovering individual variability and neurobiological brain processes, which can serve as biologically plausible measures for linking biological processes and human behavior.

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NeurIPS Conference 2022 Conference Paper

Improving Barely Supervised Learning by Discriminating Unlabeled Samples with Super-Class

Guan Gui
Zhen Zhao
Lei Qi
Luping Zhou
Lei Wang
Yinghuan Shi

In semi-supervised learning (SSL), a common practice is to learn consistent information from unlabeled data and discriminative information from labeled data to ensure both the immutability and the separability of the classification model. Existing SSL methods suffer from failures in barely-supervised learning (BSL), where only one or two labels per class are available, as the insufficient labels cause the discriminative information being difficult or even infeasible to learn. To bridge this gap, we investigate a simple yet effective way to leverage unlabeled samples for discriminative learning, and propose a novel discriminative information learning module to benefit model training. Specifically, we formulate the learning objective of discriminative information at the super-class level and dynamically assign different classes into different super-classes based on model performance improvement. On top of this on-the-fly process, we further propose a distribution-based loss to learn discriminative information by utilizing the similarity relationship between samples and super-classes. It encourages the unlabeled samples to stay closer to the distribution of their corresponding super-class than those of others. Such a constraint is softer than the direct assignment of pseudo labels, while the latter could be very noisy in BSL. We compare our method with state-of-the-art SSL and BSL methods through extensive experiments on standard SSL benchmarks. Our method can achieve superior results, \eg, an average accuracy of 76. 76\% on CIFAR-10 with merely 1 label per class.

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AAAI Conference 2022 Conference Paper

LaSSL: Label-Guided Self-Training for Semi-supervised Learning

Zhen Zhao
Luping Zhou
Lei Wang
Yinghuan Shi
Yang Gao

The key to semi-supervised learning (SSL) is to explore adequate information to leverage the unlabeled data. Current dominant approaches aim to generate pseudolabels on weakly augmented instances and train models on their corresponding strongly augmented variants with high-confidence results. However, such methods are limited in excluding samples with low-confidence pseudo-labels and under-utilization of the label information. In this paper, we emphasize the cruciality of the label information and propose a Label-guided Self-training approach to Semi-supervised Learning (LaSSL), which improves pseudo-label generations from two mutually boosted strategies. First, with the ground-truth labels and iteratively-polished pseudolabels, we explore instance relations among all samples and then minimize a class-aware contrastive loss to learn discriminative feature representations that make same-class samples gathered and different-class samples scattered. Second, on top of improved feature representations, we propagate the label information to the unlabeled samples across the potential data manifold at the feature-embedding level, which can further improve the labelling of samples with reference to their neighbours. These two strategies are seamlessly integrated and mutually promoted across the whole training process. We evaluate LaSSL on several classification benchmarks under partially labeled settings and demonstrate its superiority over the state-of-the-art approaches.

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TCS Journal 2021 Journal Article

Generic construction for tightly-secure signatures from discrete log

Jianchang Lai
Ge Wu
Peng Jiang
Zhen Zhao
Willy Susilo
Fuchun Guo

Tightly secure signature plays a significant role in the research of cryptography and has been studied extensively in the literature. In this paper, we present a generic construction for tightly-secure signatures from the discrete log (DL) assumption in the existential-unforgeability against key only attacks (EUF-KOA) security model, where the adversary is allowed to obtain only the public key, but not any sample signature. Moreover, the generic construction can also be extended into the multi-user setting with corruptions (MU-C) model. Roughly speaking, given any signature scheme, we can efficiently convert it into a signature scheme that features tight security under the DL assumption in the MU-EUF-KOA-C security model with random oracles. Our transformation shows it is easy to construct a DL-equivalent signature in the EUF-KOA security model, although many known DL-based signatures are not equivalent to DL. If the given signature scheme is key-re-randomizable, the transformed scheme is also key-re-randomizable. Hence, our result provides a supplement to Bader et al. 's work (EUROCRYPT 2016).

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TCS Journal 2020 Journal Article

Accountable authority identity-based broadcast encryption with constant-size private keys and ciphertexts

Zhen Zhao
Fuchun Guo
Jianchang Lai
Willy Susilo
Baocang Wang
Yupu Hu

Identity-based broadcast encryption (IBBE) enables a sender to broadcast a message to multiple identities efficiently. Nevertheless, since IBBE is based on identity-based cryptography (IBC), it suffers from the inherent key escrow problem. As a consequence, not only the user knows its private key, but also the private key generator (PKG). This property leads to that the creator of a given pirated private key, named a private key from an unknown source, is untraceable since both the PKG and suspected user can generate such a pirated private key for this identity. To mitigate this problem, accountable authority IBBE (A-IBBE) was proposed to provide accountability for IBBE, where white-box A-IBBE can distinguish the creator of a given pirated private key between the PKG and suspected user and black-box A-IBBE can further trace the creator of a decoder box. However, all prior constructions of black-box A-IBBE do not capture constant-size private keys and ciphertexts simultaneously. In this paper, to fill this gap, we propose a weak black-box A-IBBE scheme with constant-size private keys and ciphertexts. Our construction supports public traceability such that tracing can be performed with the public tracing key of suspected user instead of its secret key. We first define the weak black-box A-IBBE with public traceability. Then, we give our construction where the private key and ciphertext consist of two and five group elements respectively. Furthermore, the proposed scheme is proven to be secure with random oracles.

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