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Yanbin Liu

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6 papers

2 author rows

EAAI Journal 2026 Journal Article

Feature boosting and scale-aware network with multi-modal information for underwater salient object detection

Tingyu Wang
Junzhe Lu
Bin Wan
Rongfeng Lu
Yaoqi Sun
Duanpo Wu
Yanbin Liu
Chenggang Yan

Underwater salient object detection (USOD) plays an important role in marine engineering applications. Due to the poor image quality caused by the complex underwater environment, USOD remains a challenging task. Existing methods typically fuse features extracted from underwater images and depth maps to exploit more salient cues. However, they neglect the degradation and noise corruption inherent in underwater multi-modal data. Moreover, these methods pay limited attention to the scale variations of underwater objects. To address these issues, we propose a feature boosting and scale-aware network (FBS-Net), which includes two processes: underwater feature boosting and scale-aware iterative fusion decoding. The underwater feature boosting process employs a scene contrast perception module (SCPM) to improve underwater Red–Green–Blue (RGB) image feature reliability through comparative analysis of the differences between RGB images and underwater enhanced images, and a frequency domain decoupling fusion module (FDFM) to reconstruct high-quality scene representations from noisy multi-modal inputs. In the scale-aware iterative fusion decoding process, a scale-aware iterative fusion decoder (SIFD) is introduced to dynamically process multi-scale information while suppressing noise and conflicting features through multiple refinement iterations. Extensive experiments on the USOD10K, COD10K, and USOD datasets demonstrate that our proposed method outperforms 16 state-of-the-art methods. Our code and results are available at https: //github. com/llllxxx2333/FBSNet.

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ECAI Conference 2024 Conference Paper

Class-Aware Sample Weight Learning for Cross-Modal Unsupervised Domain Adaptation in Cross-User Wearable Human Activity Recognition

Tong Wu
Yanbin Liu
Sira Yongchareon

Existing unsupervised domain adaption approaches for cross-user Wearable Human Activity Recognition (WHAR) typically assume that users utilize the uni-modal sensor deployment configuration and cannot transfer across different sensor modalities. In this paper, we consider the more realistic cross-modal wearable human activity recognition setting to investigate the unsupervised domain adaptation task. This new context presents two formidable challenges: (1) how to alleviate modality heterogeneity across users, and (2) how to explore cross-modal domain correlation for better unsupervised domain adaptation. We propose a cross-modal unsupervised domain Adaptation model with Class-Aware Sample Weight Learning (CASWL-Adapt) to address both challenges. First, a spherical modality discriminator is designed to capture modal-specific discriminative features of each user during domain adaptation, thus achieving a reduction of sample variance caused by modal heterogeneity. Given a user-specific modal, modality-independent domain-invariant features can be efficiently generated by the well-developed modality discrimination loss and adversarial training. Second, a class-aware weight network is devised to calculate sample weights through classification loss and activity class similarity for each sample. Furthermore, the network leverage end-to-end learning and meta-optimization update rules to explore inter-domain correlations. Cross-modal activity classes are expected to adaptively implement different weighting schemes based on their intrinsic bias characteristics to select the most appropriate samples for domain knowledge transfer. We demonstrate that CASWL-Adapt achieves state-of-the-art results on three challenging benchmarks: Epic-Kitchens, Multimodal-EA and RealWorld, especially effective for new users of unseen modality.

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EAAI Journal 2024 Journal Article

Gaussian process fusion method for multi-fidelity data with heterogeneity distribution in aerospace vehicle flight dynamics

Ben Yang
Boyi Chen
Yanbin Liu
Jinbao Chen

In the engineering design of aerospace vehicles, design data at different stages exhibit hierarchical and heterogeneous distribution characteristics. Specifically, high-fidelity design data (such as from computational fluid dynamics simulations and flight tests) are costly and time-consuming to obtain. Moreover, the limited high-precision samples that are acquired often fail to cover the entire design space, resulting in a distribution characterized by small sample sizes. A critical challenge in data-driven modeling is efficiently fusing low-fidelity data with limited heterogeneous high-fidelity data to improve model accuracy and predictive performance. In response to this challenge, this paper introduces a Gaussian process fusion method for multi-fidelity data, founded on distribution characteristics. Multi-fidelity data are represented as intermediate surrogates using Gaussian processes, identifying heteroscedastic noise properties and deriving posterior distributions. The fusion is then treated as an optimization problem for prediction variance, using K-nearest neighbors and spatial clustering to determine optimal weights, which are adaptively adjusted based on sample density. These weights are adaptively adjusted based on the sample density to strengthen the local modeling behavior. The paper concludes with a comparative analysis, evaluating the proposed method against other conventional approaches using numerical cases and an aerodynamic prediction scenario for aerospace vehicles. A comparative analysis shows that the proposed method improves global modeling accuracy by 45% and reduces the demand for high-fidelity samples by over 40% compared to traditional methods. Applied in aerospace design, the method effectively merges multi-source data, establishing a robust hypersonic aerodynamic database while controlling modeling costs and demonstrating robustness to sample distribution.

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NeurIPS Conference 2024 Conference Paper

TPR: Topology-Preserving Reservoirs for Generalized Zero-Shot Learning

Hui Chen
Yanbin Liu
Yongqiang Ma
Nanning Zheng
Xin Yu

Pre-trained vision-language models (VLMs) such as CLIP have shown excellent performance for zero-shot classification. Based on CLIP, recent methods design various learnable prompts to evaluate the zero-shot generalization capability on a base-to-novel setting. This setting assumes test samples are already divided into either base or novel classes, limiting its application to realistic scenarios. In this paper, we focus on a more challenging and practical setting: generalized zero-shot learning (GZSL), i. e. , testing with no information about the base/novel division. To address this challenging zero-shot problem, we introduce two unique designs that enable us to classify an image without the need of knowing whether it comes from seen or unseen classes. Firstly, most existing methods only adopt a single latent space to align visual and linguistic features, which has a limited ability to represent complex visual-linguistic patterns, especially for fine-grained tasks. Instead, we propose a dual-space feature alignment module that effectively augments the latent space with a novel attribute space induced by a well-devised attribute reservoir. In particular, the attribute reservoir consists of a static vocabulary and learnable tokens complementing each other for flexible control over feature granularity. Secondly, finetuning CLIP models (e. g. , prompt learning) on seen base classes usually sacrifices the model's original generalization capability on unseen novel classes. To mitigate this issue, we present a new topology-preserving objective that can enforce feature topology structures of the combined base and novel classes to resemble the topology of CLIP. In this manner, our model will inherit the generalization ability of CLIP through maintaining the pairwise class angles in the attribute space. Extensive experiments on twelve object recognition datasets demonstrate that our model, termed Topology-Preserving Reservoir (TPR), outperforms strong baselines including both prompt learning and conventional generative-based zero-shot methods.

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EAAI Journal 2021 Journal Article

Modified Kalman particle swarm optimization: Application for trim problem of very flexible aircraft

Hao Lei
Boyi Chen
Yanbin Liu
Yuping Lv

Particle swarm optimization (PSO) is one of the most popular stochastic swarm-based metaheuristic algorithms. Kalman filter principle is introduced to predict the global optimum more accurately to enhance convergence. However, the evolution of particles in current Kalman PSO merely depends on the adjustment based on observation. In this paper, a modified Kalman particle swarm optimization (MKPSO) algorithm is proposed. The population is extended with the estimated optimum based on Kalman filtering, in which the prediction model is formulated as the weighted central optimum. Benchmark functions in the CEC14 test suite are adopted to verify the effectiveness of MKPSO. Numerical results show that MKPSO is more effective in mining capability for high-dimensional problems. Besides, the superiority of MKPSO lies in solving hybrid optimization problems. At last, MKPSO is applied to maximize the attainable moments subset of very flexible aircraft (VFA) on account for redundancy of control surfaces. Simulation results reveal that there is a trade-off between flight and control performance for VFA.

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AAAI Conference 2019 Conference Paper

Adaptive Sparse Confidence-Weighted Learning for Online Feature Selection

Yanbin Liu
Yan Yan
Ling Chen
Yahong Han
Yi Yang

In this paper, we propose a new online feature selection algorithm for streaming data. We aim to focus on the following two problems which remain unaddressed in literature. First, most existing online feature selection algorithms merely utilize the first-order information of the data streams, regardless of the fact that second-order information explores the correlations between features and significantly improves the performance. Second, most online feature selection algorithms are based on the balanced data presumption, which is not true in many real-world applications. For example, in fraud detection, the number of positive examples are much less than negative examples because most cases are not fraud. The balanced assumption will make the selected features biased towards the majority class and fail to detect the fraud cases. We propose an Adaptive Sparse Confidence-Weighted (ASCW) algorithm to solve the aforementioned two problems. We first introduce an `0-norm constraint into the second-order confidence-weighted (CW) learning for feature selection. Then the original loss is substituted with a cost-sensitive loss function to address the imbalanced data issue. Furthermore, our algorithm maintains multiple sparse CW learner with the corresponding cost vector to dynamically select an optimal cost. We theoretically enhance the theory of sparse CW learning and analyze the performance behavior in F-measure. Empirical studies show the superior performance over the stateof-the-art online learning methods in the online-batch setting.

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