arXiv subjects

Xiao Jin

Publications and source records attributed to Xiao Jin.

36 records · Page 2Linked to original sources

arXiv2025

Physical Reservoir Computing in Hook-Shaped Rover Wheel Spokes for Real-Time Terrain Identification

Effective terrain detection in unknown environments is crucial for safe and efficient robotic navigation. Traditional methods often rely on computationally intensive data processing, requiring extensive onboard computational capacity and limiting real-time performance for rovers. This study presents a novel approach that combines physical reservoir computing with piezoelectric sensors embedded in rover wheel spokes for real-time terrain identification. By leveraging wheel dynamics, terrain-induced vibrations are transformed into high-dimensional features for machine learning-based classification. Experimental results show that strategically placing three sensors on the wheel spokes achieves 90$\%$ classification accuracy, which demonstrates the accuracy and feasibility of the proposed method. The experiment results also showed that the system can effectively distinguish known terrains and identify unknown terrains by analyzing their similarity to learned categories. This method provides a robust, low-power framework for real-time terrain classification and roughness estimation in unstructured environments, enhancing rover autonomy and adaptability.

cs.RO↗

arXiv2025

Multi-Sensor Fusion-Based Mobile Manipulator Remote Control for Intelligent Smart Home Assistance

This paper proposes a wearable-controlled mobile manipulator system for intelligent smart home assistance, integrating MEMS capacitive microphones, IMU sensors, vibration motors, and pressure feedback to enhance human-robot interaction. The wearable device captures forearm muscle activity and converts it into real-time control signals for mobile manipulation. The wearable device achieves an offline classification accuracy of 88.33\%\ across six distinct movement-force classes for hand gestures by using a CNN-LSTM model, while real-world experiments involving five participants yield a practical accuracy of 83.33\%\ with an average system response time of 1.2 seconds. In Human-Robot synergy in navigation and grasping tasks, the robot achieved a 98\%\ task success rate with an average trajectory deviation of only 3.6 cm. Finally, the wearable-controlled mobile manipulator system achieved a 93.3\%\ gripping success rate, a transfer success of 95.6\%\, and a full-task success rate of 91.1\%\ during object grasping and transfer tests, in which a total of 9 object-texture combinations were evaluated. These three experiments' results validate the effectiveness of MEMS-based wearable sensing combined with multi-sensor fusion for reliable and intuitive control of assistive robots in smart home scenarios.

cs.RO↗

arXiv2025

Exploring Few-Shot Defect Segmentation in General Industrial Scenarios with Metric Learning and Vision Foundation Models

Industrial defect segmentation is critical for manufacturing quality control. Due to the scarcity of training defect samples, few-shot semantic segmentation (FSS) holds significant value in this field. However, existing studies mostly apply FSS to tackle defects on simple textures, without considering more diverse scenarios. This paper aims to address this gap by exploring FSS in broader industrial products with various defect types. To this end, we contribute a new real-world dataset and reorganize some existing datasets to build a more comprehensive few-shot defect segmentation (FDS) benchmark. On this benchmark, we thoroughly investigate metric learning-based FSS methods, including those based on meta-learning and those based on Vision Foundation Models (VFMs). We observe that existing meta-learning-based methods are generally not well-suited for this task, while VFMs hold great potential. We further systematically study the applicability of various VFMs in this task, involving two paradigms: feature matching and the use of Segment Anything (SAM) models. We propose a novel efficient FDS method based on feature matching. Meanwhile, we find that SAM2 is particularly effective for addressing FDS through its video track mode. The contributed dataset and code will be available at: https://github.com/liutongkun/GFDS.

cs.CV↗

arXiv2024

Diff-Cleanse: Identifying and Mitigating Backdoor Attacks in Diffusion Models

Diffusion models (DMs) are regarded as one of the most advanced generative models today, yet recent studies suggest that they are vulnerable to backdoor attacks, which establish hidden associations between particular input patterns and model behaviors, compromising model integrity by causing undesirable actions with manipulated inputs. This vulnerability poses substantial risks, including reputational damage to model owners and the dissemination of harmful content. To mitigate the threat of backdoor attacks, there have been some investigations on backdoor detection and model repair. However, previous work fails to reliably purify the models backdoored by state-of-the-art attack methods, rendering the field much underexplored. To bridge this gap, we introduce Diff-Cleanse, a novel two-stage backdoor defense framework specifically designed for DMs. The first stage employs a novel trigger inversion technique to reconstruct the trigger and detect the backdoor, and the second stage utilizes a structural pruning method to eliminate the backdoor. We evaluate our framework on hundreds of DMs that are attacked by three existing backdoor attack methods with a wide range of hyperparameter settings. Extensive experiments demonstrate that Diff-Cleanse achieves nearly 100\% detection accuracy and effectively mitigates backdoor impacts, preserving the model's benign performance with minimal compromise. Our code is avaliable at https://github.com/shymuel/diff-cleanse.

cs.CR↗

arXiv2024

Low Excess Noise, High Quantum Efficiency Avalanche Photodiodes for Beyond 2 μm Wavelength Detection

The increasing concentration of greenhouse gases, notably CH4 and CO2, has fueled global temperature increases, intensifying concerns regarding the prevailing climate crisis. Effectively monitoring these gases demands a detector spanning the extended short-wavelength infrared (~2.4 μm) range, covering wavelengths of CH4 (1.65 μm) and CO2 (2.05 μm). The state-of-the-art HgCdTe avalanche photodetectors (APDs) offer exceptional performance metrics, including high gain (M) and low excess noise (F). However, their widespread adoption is hindered by inherent challenges such as manufacturability, reproducibility, and cost factors. Moreover, their reliance on cryogenic cooling adds to the cost, size, weight, and power of the system. We have demonstrated a linear mode APD combining an InGaAs/GaAsSb type-II superlattice absorber and an AlGaAsSb multiplier lattice matched to InP substrates. This APD has demonstrated a room temperature M of 178, a maximum measurable external quantum efficiency of 3560 % at 2 μm, an extremely low excess noise (F < 2 at M < 20), and a small temperature coefficient of breakdown (7.58 mV/K μm). Such a high performance APD with manufacturable semiconductor materials could lead to a rapid transition to a commercial III-V foundry, holding the promise of revolutionizing high-sensitivity receivers for greenhouse gas monitoring.

cond-mat.mtrl-sci↗

arXiv2023

Contextual Optimizer through Neighborhood Estimation for prescriptive analysis

We address the challenges posed by heteroscedastic noise in contextual decision-making. We propose a consistent Shrinking Neighborhood Estimation (SNE) technique that successfully estimates contextual performance under unpredictable variances. Furthermore, we propose a Rate-Efficient Sampling rule designed to enhance the performance of the SNE. The effectiveness of the combined solution ``Contextual Optimizer through Neighborhood Estimation"(CONE) is validated through theorems and numerical benchmarking. The methodologies have been further deployed to address a staffing challenge in a hospital call center, exemplifying their substantial impact and practical utility in real-world scenarios.

math.OC↗

arXiv2023

HODINet: High-Order Discrepant Interaction Network for RGB-D Salient Object Detection

RGB-D salient object detection (SOD) aims to detect the prominent regions by jointly modeling RGB and depth information. Most RGB-D SOD methods apply the same type of backbones and fusion modules to identically learn the multimodality and multistage features. However, these features contribute differently to the final saliency results, which raises two issues: 1) how to model discrepant characteristics of RGB images and depth maps; 2) how to fuse these cross-modality features in different stages. In this paper, we propose a high-order discrepant interaction network (HODINet) for RGB-D SOD. Concretely, we first employ transformer-based and CNN-based architectures as backbones to encode RGB and depth features, respectively. Then, the high-order representations are delicately extracted and embedded into spatial and channel attentions for cross-modality feature fusion in different stages. Specifically, we design a high-order spatial fusion (HOSF) module and a high-order channel fusion (HOCF) module to fuse features of the first two and the last two stages, respectively. Besides, a cascaded pyramid reconstruction network is adopted to progressively decode the fused features in a top-down pathway. Extensive experiments are conducted on seven widely used datasets to demonstrate the effectiveness of the proposed approach. We achieve competitive performance against 24 state-of-the-art methods under four evaluation metrics.

cs.CV↗

arXiv2023

Searching for the Fakes: Efficient Neural Architecture Search for General Face Forgery Detection

As the saying goes, "seeing is believing". However, with the development of digital face editing tools, we can no longer trust what we can see. Although face forgery detection has made promising progress, most current methods are designed manually by human experts, which is labor-consuming. In this paper, we develop an end-to-end framework based on neural architecture search (NAS) for deepfake detection, which can automatically design network architectures without human intervention. First, a forgery-oriented search space is created to choose appropriate operations for this task. Second, we propose a novel performance estimation metric, which guides the search process to select more general models. The cross-dataset search is also considered to develop more general architectures. Eventually, we connect the cells in a cascaded pyramid way for final forgery classification. Compared with state-of-the-art networks artificially designed, our method achieves competitive performance in both in-dataset and cross-dataset scenarios.

cs.CV↗

arXiv2023

Component-aware anomaly detection framework for adjustable and logical industrial visual inspection

Industrial visual inspection aims at detecting surface defects in products during the manufacturing process. Although existing anomaly detection models have shown great performance on many public benchmarks, their limited adjustability and ability to detect logical anomalies hinder their broader use in real-world settings. To this end, in this paper, we propose a novel component-aware anomaly detection framework (ComAD) which can simultaneously achieve adjustable and logical anomaly detection for industrial scenarios. Specifically, we propose to segment images into multiple components based on a lightweight and nearly training-free unsupervised semantic segmentation model. Then, we design an interpretable logical anomaly detection model through modeling the metrological features of each component and their relationships. Despite its simplicity, our framework achieves state-of-the-art performance on image-level logical anomaly detection. Meanwhile, segmenting a product image into multiple components provides a novel perspective for industrial visual inspection, demonstrating great potential in model customization, noise resistance, and anomaly classification. The code will be available at https://github.com/liutongkun/ComAD.

cs.CV↗

arXiv2022

TriPINet: Tripartite Progressive Integration Network for Image Manipulation Localization

Image manipulation localization aims at distinguishing forged regions from the whole test image. Although many outstanding prior arts have been proposed for this task, there are still two issues that need to be further studied: 1) how to fuse diverse types of features with forgery clues; 2) how to progressively integrate multistage features for better localization performance. In this paper, we propose a tripartite progressive integration network (TriPINet) for end-to-end image manipulation localization. First, we extract both visual perception information, e.g., RGB input images, and visual imperceptible features, e.g., frequency and noise traces for forensic feature learning. Second, we develop a guided cross-modality dual-attention (gCMDA) module to fuse different types of forged clues. Third, we design a set of progressive integration squeeze-and-excitation (PI-SE) modules to improve localization performance by appropriately incorporating multiscale features in the decoder. Extensive experiments are conducted to compare our method with state-of-the-art image forensics approaches. The proposed TriPINet obtains competitive results on several benchmark datasets.

cs.CV↗

arXiv2022

CAFE: Catastrophic Data Leakage in Vertical Federated Learning

Recent studies show that private training data can be leaked through the gradients sharing mechanism deployed in distributed machine learning systems, such as federated learning (FL). Increasing batch size to complicate data recovery is often viewed as a promising defense strategy against data leakage. In this paper, we revisit this defense premise and propose an advanced data leakage attack with theoretical justification to efficiently recover batch data from the shared aggregated gradients. We name our proposed method as catastrophic data leakage in vertical federated learning (CAFE). Comparing to existing data leakage attacks, our extensive experimental results on vertical FL settings demonstrate the effectiveness of CAFE to perform large-batch data leakage attack with improved data recovery quality. We also propose a practical countermeasure to mitigate CAFE. Our results suggest that private data participated in standard FL, especially the vertical case, have a high risk of being leaked from the training gradients. Our analysis implies unprecedented and practical data leakage risks in those learning settings. The code of our work is available at https://github.com/DeRafael/CAFE.

cs.LG↗

arXiv2021

S3RP: Self-Supervised Super-Resolution and Prediction for Advection-Diffusion Process

We present a super-resolution model for an advection-diffusion process with limited information. While most of the super-resolution models assume high-resolution (HR) ground-truth data in the training, in many cases such HR dataset is not readily accessible. Here, we show that a Recurrent Convolutional Network trained with physics-based regularizations is able to reconstruct the HR information without having the HR ground-truth data. Moreover, considering the ill-posed nature of a super-resolution problem, we employ the Recurrent Wasserstein Autoencoder to model the uncertainty.

cs.LG↗

arXiv2021

RSKNet-MTSP: Effective and Portable Deep Architecture for Speaker Verification

The convolutional neural network (CNN) based approaches have shown great success for speaker verification (SV) tasks, where modeling long temporal context and reducing information loss of speaker characteristics are two important challenges significantly affecting the verification performance. Previous works have introduced dilated convolution and multi-scale aggregation methods to address above challenges. However, such methods are also hard to make full use of some valuable information, which make it difficult to substantially improve the verification performance. To address above issues, we construct a novel CNN-based architecture for SV, called RSKNet-MTSP, where a residual selective kernel block (RSKBlock) and a multiple time-scale statistics pooling (MTSP) module are first proposed. The RSKNet-MTSP can capture both long temporal context and neighbouring information, and gather more speaker-discriminative information from multi-scale features. In order to design a portable model for real applications with limited resources, we then present a lightweight version of RSKNet-MTSP, namely RSKNet-MTSP-L, which employs a combination technique associating the depthwise separable convolutions with low-rank factorization of weight matrices. Extensive experiments are conducted on two public SV datasets, VoxCeleb and Speaker in the Wild (SITW). The results demonstrate that 1) RSKNet-MTSP outperforms the state-of-the-art deep embedding architectures by at least 9%-26% in all test sets. 2) RSKNet-MTSP-L achieves competitive performance compared with baseline models with 17%-39% less network parameters. The ablation experiments further illustrate that our proposed approaches can achieve substantial improvement over prior methods.

cs.SD↗

arXiv2020

VAFL: a Method of Vertical Asynchronous Federated Learning

Horizontal Federated learning (FL) handles multi-client data that share the same set of features, and vertical FL trains a better predictor that combine all the features from different clients. This paper targets solving vertical FL in an asynchronous fashion, and develops a simple FL method. The new method allows each client to run stochastic gradient algorithms without coordination with other clients, so it is suitable for intermittent connectivity of clients. This method further uses a new technique of perturbed local embedding to ensure data privacy and improve communication efficiency. Theoretically, we present the convergence rate and privacy level of our method for strongly convex, nonconvex and even nonsmooth objectives separately. Empirically, we apply our method to FL on various image and healthcare datasets. The results compare favorably to centralized and synchronous FL methods.

cs.LG↗

arXiv2020

Martingale structure for general thermodynamic functionals of diffusion processes under second-order averaging

Novel hidden thermodynamic structures have recently been uncovered during the investigation of nonequilibrium thermodynamics for multiscale stochastic processes. Here we reveal the martingale structure for a general thermodynamic functional of inhomogeneous singularly perturbed diffusion processes under second-order averaging, where a general thermodynamic functional is defined as the logarithmic Radon-Nykodim derivative between the laws of the original process and a comparable process (forward case) or its time reversal (backward case). In the forward case, we prove that the regular and anomalous parts of a thermodynamic functional are orthogonal martingales. In the backward case, while the regular part may not be a martingale, we prove that the anomalous part is still a martingale. With the aid of the martingale structure, we prove the integral fluctuation theorem satisfied by the regular and anomalous parts of a general thermodynamic functional. Further extensions and applications to stochastic thermodynamics are also discussed, including the martingale structure and fluctuation theorems for the regular and anomalous parts of entropy production and housekeeping heat in the absence or presence of odd variables.

math-ph↗

arXiv2019

Knowledge Distillation via Route Constrained Optimization

Distillation-based learning boosts the performance of the miniaturized neural network based on the hypothesis that the representation of a teacher model can be used as structured and relatively weak supervision, and thus would be easily learned by a miniaturized model. However, we find that the representation of a converged heavy model is still a strong constraint for training a small student model, which leads to a high lower bound of congruence loss. In this work, inspired by curriculum learning we consider the knowledge distillation from the perspective of curriculum learning by routing. Instead of supervising the student model with a converged teacher model, we supervised it with some anchor points selected from the route in parameter space that the teacher model passed by, as we called route constrained optimization (RCO). We experimentally demonstrate this simple operation greatly reduces the lower bound of congruence loss for knowledge distillation, hint and mimicking learning. On close-set classification tasks like CIFAR100 and ImageNet, RCO improves knowledge distillation by 2.14% and 1.5% respectively. For the sake of evaluating the generalization, we also test RCO on the open-set face recognition task MegaFace.

cs.LG↗

arXiv2019

Correlation Congruence for Knowledge Distillation

Most teacher-student frameworks based on knowledge distillation (KD) depend on a strong congruent constraint on instance level. However, they usually ignore the correlation between multiple instances, which is also valuable for knowledge transfer. In this work, we propose a new framework named correlation congruence for knowledge distillation (CCKD), which transfers not only the instance-level information, but also the correlation between instances. Furthermore, a generalized kernel method based on Taylor series expansion is proposed to better capture the correlation between instances. Empirical experiments and ablation studies on image classification tasks (including CIFAR-100, ImageNet-1K) and metric learning tasks (including ReID and Face Recognition) show that the proposed CCKD substantially outperforms the original KD and achieves state-of-the-art accuracy compared with other SOTA KD-based methods. The CCKD can be easily deployed in the majority of the teacher-student framework such as KD and hint-based learning methods.

cs.CV↗

arXiv2016

Nonequilibrium steady state of biochemical cycle kinetics under non-isothermal conditions

Nonequilibrium steady state of isothermal biochemical cycle kinetics has been extensively studied, but much less investigated under non-isothermal conditions. However, once the heat exchange between subsystems is rather slow, the isothermal assumption of the whole system meets great challenge, which is indeed the case inside many kinds of living organisms. Here we generalize the nonequilibrium steady-state theory of isothermal biochemical cycle kinetics, in the master-equation models, to the situation in which the temperatures of subsystems can be far from uniform. We first obtain a new thermodynamic relation between the chemical reaction rates and thermodynamic potentials under such a non-isothermal circumstances, which immediately implies simply applying the isothermal transition-state rate formula for each chemical reaction in terms of only the reactants' temperature, is not thermodynamically consistent. Therefore, we mathematically derive several revised reaction-rate formulas which not only obey the new thermodynamic relation but also approximate the exact reaction rate better than the rate formula under isothermal condition. The new thermodynamic relation also predicts that in the transporter system with different temperatures inside and outside the membrane, the net flux of the transported molecules can possibly even go against the temperature gradient in the absence of the chemical driving force.

physics.chem-ph↗