arXiv subjects

Xin Luo

Publications and source records attributed to Xin Luo.

At least 55 records · Page 3Linked to original sources

arXiv2025

NTIRE 2025 Challenge on RAW Image Restoration and Super-Resolution

This paper reviews the NTIRE 2025 RAW Image Restoration and Super-Resolution Challenge, highlighting the proposed solutions and results. New methods for RAW Restoration and Super-Resolution could be essential in modern Image Signal Processing (ISP) pipelines, however, this problem is not as explored as in the RGB domain. The goal of this challenge is two fold, (i) restore RAW images with blur and noise degradations, (ii) upscale RAW Bayer images by 2x, considering unknown noise and blur. In the challenge, a total of 230 participants registered, and 45 submitted results during thee challenge period. This report presents the current state-of-the-art in RAW Restoration.

eess.IV↗

arXiv2025

DATA: Multi-Disentanglement based Contrastive Learning for Open-World Semi-Supervised Deepfake Attribution

Deepfake attribution (DFA) aims to perform multiclassification on different facial manipulation techniques, thereby mitigating the detrimental effects of forgery content on the social order and personal reputations. However, previous methods focus only on method-specific clues, which easily lead to overfitting, while overlooking the crucial role of common forgery features. Additionally, they struggle to distinguish between uncertain novel classes in more practical open-world scenarios. To address these issues, in this paper we propose an innovative multi-DisentAnglement based conTrastive leArning framework, DATA, to enhance the generalization ability on novel classes for the open-world semi-supervised deepfake attribution (OSS-DFA) task. Specifically, since all generation techniques can be abstracted into a similar architecture, DATA defines the concept of 'Orthonormal Deepfake Basis' for the first time and utilizes it to disentangle method-specific features, thereby reducing the overfitting on forgery-irrelevant information. Furthermore, an augmented-memory mechanism is designed to assist in novel class discovery and contrastive learning, which aims to obtain clear class boundaries for the novel classes through instance-level disentanglements. Additionally, to enhance the standardization and discrimination of features, DATA uses bases contrastive loss and center contrastive loss as auxiliaries for the aforementioned modules. Extensive experimental evaluations show that DATA achieves state-of-the-art performance on the OSS-DFA benchmark, e.g., there are notable accuracy improvements in 2.55% / 5.7% under different settings, compared with the existing methods.

cs.CV↗

arXiv2025

Learning Real Facial Concepts for Independent Deepfake Detection

Deepfake detection models often struggle with generalization to unseen datasets, manifesting as misclassifying real instances as fake in target domains. This is primarily due to an overreliance on forgery artifacts and a limited understanding of real faces. To address this challenge, we propose a novel approach RealID to enhance generalization by learning a comprehensive concept of real faces while assessing the probabilities of belonging to the real and fake classes independently. RealID comprises two key modules: the Real Concept Capture Module (RealC2) and the Independent Dual-Decision Classifier (IDC). With the assistance of a MultiReal Memory, RealC2 maintains various prototypes for real faces, allowing the model to capture a comprehensive concept of real class. Meanwhile, IDC redefines the classification strategy by making independent decisions based on the concept of the real class and the presence of forgery artifacts. Through the combined effect of the above modules, the influence of forgery-irrelevant patterns is alleviated, and extensive experiments on five widely used datasets demonstrate that RealID significantly outperforms existing state-of-the-art methods, achieving a 1.74% improvement in average accuracy.

cs.CV↗

arXiv2025

Concept Factorization via Self-Representation and Adaptive Graph Structure Learning

Concept Factorization (CF) models have attracted widespread attention due to their excellent performance in data clustering. In recent years, many variant models based on CF have achieved great success in clustering by taking into account the internal geometric manifold structure of the dataset and using graph regularization techniques. However, their clustering performance depends greatly on the construction of the initial graph structure. In order to enable adaptive learning of the graph structure of the data, we propose a Concept Factorization Based on Self-Representation and Adaptive Graph Structure Learning (CFSRAG) Model. CFSRAG learns the affinity relationship between data through a self-representation method, and uses the learned affinity matrix to implement dynamic graph regularization constraints, thereby ensuring dynamic learning of the internal geometric structure of the data. Finally, we give the CFSRAG update rule and convergence analysis, and conduct comparative experiments on four real datasets. The results show that our model outperforms other state-of-the-art models.

cs.LG↗

arXiv2025

The effect of ion rotational flow on Hall thruster azimuthal instability via two dimensional PIC simulations

Previous experimental studies have found that the neutral gas rotational flow in the opposite direction of electron Hall drift can lead to better experimental results comparing to the same direction. In Hall thrusters, the core factor influencing operational states is the electron cross field transport, where the azimuthal instability serves as a key mechanism. The rotational flow of neutral gas may affect instability by altering initial azimuthal velocity of ions, which has not been investigated before. Therefore, to study the effects of ion rotational flow of varying magnitudes and directions on azimuthal instability, simulations are conducted in this work based on two benchmark particle-in-cell (PIC) cases: the azimuthal-axial and the azimuthal-radial. The results indicate that the ion rotational flow velocity can potentially complicate the coupling characteristics of the electron cyclotron drifting instability and the modified two stream instability, particularly when a reverse rotational flow velocity is added. In general, both co-directional and reverse ion rotational flow have been observed to inhibit azimuthal instability, which results in a decrease in axial electron mobility. A 1% addition of the ion rotational flow (compared to the electron drift) would result in a 10% change of the electron mobility due to varied azimuthal instability, and the decrease in electron mobility of the reverse ion rotational flow is greater than that of co-directional. In addition, detailed spectral analyses are carried out to study the relation between ECDI, MTSI, and resonant wave-wave interactions.

physics.plasm-ph↗

arXiv2025

Impact of Azimuthal Magnetic Field Inhomogeneity on Hall Thruster high-frequency azimuthal instability via 2D radial-azimuthal PIC simulations

For the SPT-type Hall thrusters, the magnetic structure with magnetic conductive columns leads inherent to azimuthally inhomogeneous magnetic configurations. This azimuthal magnetic inhomogeneity may impact electron azimuthal closed-drift motion and cross-field transport characteristics. This study systematically investigates the effects of azimuthal magnetic field gradient on high frequency azimuthal instability and associated anomalous electron transport through 2D radial-azimuthal Particle-in-Cell (PIC) simulations. The results reveal dual mechanisms of magnetic inhomogeneity on electron cyclotron drift instability (ECDI) characteristics: (1) The azimuthal drift velocity distribution becomes modulated by the magnetic field inhomogeneity, with increased average drift velocity enhancing ECDI intensity under stronger inhomogeneity; (2) Simultaneously, the ECDI wavenumber spectrum broadens with elevated magnetic inhomogeneity, reducing discrete ECDI spectral peaks. Under the dual influence of magnetic field inhomogeneity, when the inhomogeneity level is below 5%, the ECDI saturation amplitude and electron cross field mobility remains largely unchanged. However, a notable reduction of 13.4% in ECDI saturation intensity and a 15.7% decrease in electron mobility are observed when magnetic field inhomogeneity reaches 10%.

physics.plasm-ph↗

arXiv2025

Federated Class-Incremental Learning with Prompting

As Web technology continues to develop, it has become increasingly common to use data stored on different clients. At the same time, federated learning has received widespread attention due to its ability to protect data privacy when let models learn from data which is distributed across various clients. However, most existing works assume that the client's data are fixed. In real-world scenarios, such an assumption is most likely not true as data may be continuously generated and new classes may also appear. To this end, we focus on the practical and challenging federated class-incremental learning (FCIL) problem. For FCIL, the local and global models may suffer from catastrophic forgetting on old classes caused by the arrival of new classes and the data distributions of clients are non-independent and identically distributed (non-iid). In this paper, we propose a novel method called Federated Class-Incremental Learning with PrompTing (FCILPT). Given the privacy and limited memory, FCILPT does not use a rehearsal-based buffer to keep exemplars of old data. We choose to use prompts to ease the catastrophic forgetting of the old classes. Specifically, we encode the task-relevant and task-irrelevant knowledge into prompts, preserving the old and new knowledge of the local clients and solving the problem of catastrophic forgetting. We first sort the task information in the prompt pool in the local clients to align the task information on different clients before global aggregation. It ensures that the same task's knowledge are fully integrated, solving the problem of non-iid caused by the lack of classes among different clients in the same incremental task. Experiments on CIFAR-100, Mini-ImageNet, and Tiny-ImageNet demonstrate that FCILPT achieves significant accuracy improvements over the state-of-the-art methods.

cs.CV↗

arXiv2025

Gamma-Ray Bursts Calibrated from the Observational $H(z)$ Data in Artificial Neural Network Framework

In this paper, we calibrate the luminosity relation of gamma-ray bursts (GRBs) from an Artificial Neural Network (ANN) framework for reconstructing the Hubble parameter \unboldmath{$H(z)$} from the latest observational Hubble data (OHD) obtained with the cosmic chronometers method in a cosmology-independent way. We consider the physical relationships between the data to introduce the covariance matrix and KL divergence of the data into the loss function and calibrate the Amati relation ($E_{\rm p}$--$E_{\rm iso}$) by selecting the optimal ANN model with the A219 sample and the J220 sample at low redshift. Combining the Pantheon+ type Ia supernovae (SNe Ia) sample and Baryon acoustic oscillations (BAOs) from Dark Energy Spectroscopy Instrument (DESI) with GRBs at high redshift in the Hubble diagram by Markov Chain Monte Carlo numerical method, we find that the $Λ$CDM model is preferred over the $w$CDM and CPL models with joint constraints by the Akaike Information Criterion and Bayesian Information Criterion.

astro-ph.CO↗

arXiv2025

Exploiting Diffusion Prior for Real-World Image Dehazing with Unpaired Training

Unpaired training has been verified as one of the most effective paradigms for real scene dehazing by learning from unpaired real-world hazy and clear images. Although numerous studies have been proposed, current methods demonstrate limited generalization for various real scenes due to limited feature representation and insufficient use of real-world prior. Inspired by the strong generative capabilities of diffusion models in producing both hazy and clear images, we exploit diffusion prior for real-world image dehazing, and propose an unpaired framework named Diff-Dehazer. Specifically, we leverage diffusion prior as bijective mapping learners within the CycleGAN, a classic unpaired learning framework. Considering that physical priors contain pivotal statistics information of real-world data, we further excavate real-world knowledge by integrating physical priors into our framework. Furthermore, we introduce a new perspective for adequately leveraging the representation ability of diffusion models by removing degradation in image and text modalities, so as to improve the dehazing effect. Extensive experiments on multiple real-world datasets demonstrate the superior performance of our method. Our code https://github.com/ywxjm/Diff-Dehazer.

cs.CV↗

arXiv2025

Scale-Invariant Adversarial Attack against Arbitrary-scale Super-resolution

The advent of local continuous image function (LIIF) has garnered significant attention for arbitrary-scale super-resolution (SR) techniques. However, while the vulnerabilities of fixed-scale SR have been assessed, the robustness of continuous representation-based arbitrary-scale SR against adversarial attacks remains an area warranting further exploration. The elaborately designed adversarial attacks for fixed-scale SR are scale-dependent, which will cause time-consuming and memory-consuming problems when applied to arbitrary-scale SR. To address this concern, we propose a simple yet effective ``scale-invariant'' SR adversarial attack method with good transferability, termed SIAGT. Specifically, we propose to construct resource-saving attacks by exploiting finite discrete points of continuous representation. In addition, we formulate a coordinate-dependent loss to enhance the cross-model transferability of the attack. The attack can significantly deteriorate the SR images while introducing imperceptible distortion to the targeted low-resolution (LR) images. Experiments carried out on three popular LIIF-based SR approaches and four classical SR datasets show remarkable attack performance and transferability of SIAGT.

cs.CV↗

arXiv2024

NeRF Inpainting with Geometric Diffusion Prior and Balanced Score Distillation

Recent advances in NeRF inpainting have leveraged pretrained diffusion models to enhance performance. However, these methods often yield suboptimal results due to their ineffective utilization of 2D diffusion priors. The limitations manifest in two critical aspects: the inadequate capture of geometric information by pretrained diffusion models and the suboptimal guidance provided by existing Score Distillation Sampling (SDS) methods. To address these problems, we introduce GB-NeRF, a novel framework that enhances NeRF inpainting through improved utilization of 2D diffusion priors. Our approach incorporates two key innovations: a fine-tuning strategy that simultaneously learns appearance and geometric priors and a specialized normal distillation loss that integrates these geometric priors into NeRF inpainting. We propose a technique called Balanced Score Distillation (BSD) that surpasses existing methods such as Score Distillation (SDS) and the improved version, Conditional Score Distillation (CSD). BSD offers improved inpainting quality in appearance and geometric aspects. Extensive experiments show that our method provides superior appearance fidelity and geometric consistency compared to existing approaches.

cs.CV↗

arXiv2024

Optimized Cryo-CMOS Technology with VTH<0.2V and Ion>1.2mA/um for High-Peformance Computing

We report the design-technology co-optimization (DTCO) scheme to develop a 28-nm cryogenic CMOS (Cryo-CMOS) technology for high-performance computing (HPC). The precise adjustment of halo implants manages to compensate the threshold voltage (VTH) shift at low temperatures. The optimized NMOS and PMOS transistors, featured by VTH<0.2V, sub-threshold swing (SS)<30 mV/dec, and on-state current (Ion)>1.2mA/um at 77K, warrant a reliable sub-0.6V operation. Moreover, the enhanced driving strength of Cryo-CMOS inherited from a higher transconductance leads to marked improvements in elevating the ring oscillator frequency by 20%, while reducing the power consumption of the compute-intensive cryogenic IC system by 37% at 77K.

eess.SY↗

arXiv2024

End-to-end Graph Learning Approach for Cognitive Diagnosis of Student Tutorial

Cognitive diagnosis (CD) utilizes students' existing studying records to estimate their mastery of unknown knowledge concepts, which is vital for evaluating their learning abilities. Accurate CD is extremely challenging because CD is associated with complex relationships and mechanisms among students, knowledge concepts, studying records, etc. However, existing approaches loosely consider these relationships and mechanisms by a non-end-to-end learning framework, resulting in sub-optimal feature extractions and fusions for CD. Different from them, this paper innovatively proposes an End-to-end Graph Neural Networks-based Cognitive Diagnosis (EGNN-CD) model. EGNN-CD consists of three main parts: knowledge concept network (KCN), graph neural networks-based feature extraction (GNNFE), and cognitive ability prediction (CAP). First, KCN constructs CD-related interaction by comprehensively extracting physical information from students, exercises, and knowledge concepts. Second, a four-channel GNNFE is designed to extract high-order and individual features from the constructed KCN. Finally, CAP employs a multi-layer perceptron to fuse the extracted features to predict students' learning abilities in an end-to-end learning way. With such designs, the feature extractions and fusions are guaranteed to be comprehensive and optimal for CD. Extensive experiments on three real datasets demonstrate that our EGNN-CD achieves significantly higher accuracy than state-of-the-art models in CD.

cs.LG↗

arXiv2024

EVC-MF: End-to-end Video Captioning Network with Multi-scale Features

Conventional approaches for video captioning leverage a variety of offline-extracted features to generate captions. Despite the availability of various offline-feature-extractors that offer diverse information from different perspectives, they have several limitations due to fixed parameters. Concretely, these extractors are solely pre-trained on image/video comprehension tasks, making them less adaptable to video caption datasets. Additionally, most of these extractors only capture features prior to the classifier of the pre-training task, ignoring a significant amount of valuable shallow information. Furthermore, employing multiple offline-features may introduce redundant information. To address these issues, we propose an end-to-end encoder-decoder-based network (EVC-MF) for video captioning, which efficiently utilizes multi-scale visual and textual features to generate video descriptions. Specifically, EVC-MF consists of three modules. Firstly, instead of relying on multiple feature extractors, we directly feed video frames into a transformer-based network to obtain multi-scale visual features and update feature extractor parameters. Secondly, we fuse the multi-scale features and input them into a masked encoder to reduce redundancy and encourage learning useful features. Finally, we utilize an enhanced transformer-based decoder, which can efficiently leverage shallow textual information, to generate video descriptions. To evaluate our proposed model, we conduct extensive experiments on benchmark datasets. The results demonstrate that EVC-MF yields competitive performance compared with the state-of-theart methods.

cs.CV↗

arXiv2024

Enhancing HOI Detection with Contextual Cues from Large Vision-Language Models

Human-Object Interaction (HOI) detection aims at detecting human-object pairs and predicting their interactions. However, conventional HOI detection methods often struggle to fully capture the contextual information needed to accurately identify these interactions. While large Vision-Language Models (VLMs) show promise in tasks involving human interactions, they are not tailored for HOI detection. The complexity of human behavior and the diverse contexts in which these interactions occur make it further challenging. Contextual cues, such as the participants involved, body language, and the surrounding environment, play crucial roles in predicting these interactions, especially those that are unseen or ambiguous. Moreover, large VLMs are trained on vast image and text data, enabling them to generate contextual cues that help in understanding real-world contexts, object relationships, and typical interactions. Building on this, in this paper we introduce ConCue, a novel approach for improving visual feature extraction in HOI detection. Specifically, we first design specialized prompts to utilize large VLMs to generate contextual cues within an image. To fully leverage these cues, we develop a transformer-based feature extraction module with a multi-tower architecture that integrates contextual cues into both instance and interaction detectors. Extensive experiments and analyses demonstrate the effectiveness of using these contextual cues for HOI detection. The experimental results show that integrating ConCue with existing state-of-the-art methods significantly enhances their performance on two widely used datasets.

cs.CV↗

arXiv2024

Robust Input Shaping Vibration Control via Extended Kalman Filter-Incorporated Residual Neural Network

With the rapid development of industry, the vibration control of flexible structures and underactuated systems has been increasingly gaining attention. Input shaping technology enables stable performance for high-speed motion in industrial motion systems. However, existing input shapers generally suffer from the ineffective control performance due to the neglect of observation errors. To address this critical issue, this paper proposes an Extended Kalman Filter-incorporated Residual Neural Network-based input Shaping (ERS) model for vibration control. Its main ideas are two-fold: a) adopting an extended Kalman filter to address a vertical flexible beam's model errors; and b) adopting a residual neural network to cascade with the extended Kalman filter for eliminating the remaining observation errors. Detailed experiments on a real dataset collected from a vertical flexible beam demonstrate that the proposed ERS model has achieved significant vibration control performance over several state-of-the-art models.

eess.SY↗

arXiv2024

Multi-Level Sequence Denoising with Cross-Signal Contrastive Learning for Sequential Recommendation

Sequential recommender systems (SRSs) aim to suggest next item for a user based on her historical interaction sequences. Recently, many research efforts have been devoted to attenuate the influence of noisy items in sequences by either assigning them with lower attention weights or discarding them directly. The major limitation of these methods is that the former would still prone to overfit noisy items while the latter may overlook informative items. To the end, in this paper, we propose a novel model named Multi-level Sequence Denoising with Cross-signal Contrastive Learning (MSDCCL) for sequential recommendation. To be specific, we first introduce a target-aware user interest extractor to simultaneously capture users' long and short term interest with the guidance of target items. Then, we develop a multi-level sequence denoising module to alleviate the impact of noisy items by employing both soft and hard signal denoising strategies. Additionally, we extend existing curriculum learning by simulating the learning pattern of human beings. It is worth noting that our proposed model can be seamlessly integrated with a majority of existing recommendation models and significantly boost their effectiveness. Experimental studies on five public datasets are conducted and the results demonstrate that the proposed MSDCCL is superior to the state-of-the-art baselines. The source code is publicly available at https://github.com/lalunex/MSDCCL/tree/main.

cs.IR↗

arXiv2024

High-level Codes and Fine-grained Weights for Online Multi-modal Hashing Retrieval

In the real world, multi-modal data often appears in a streaming fashion, and there is a growing demand for similarity retrieval from such non-stationary data, especially at a large scale. In response to this need, online multi-modal hashing has gained significant attention. However, existing online multi-modal hashing methods face challenges related to the inconsistency of hash codes during long-term learning and inefficient fusion of different modalities. In this paper, we present a novel approach to supervised online multi-modal hashing, called High-level Codes, Fine-grained Weights (HCFW). To address these problems, HCFW is designed by its non-trivial contributions from two primary dimensions: 1) Online Hashing Perspective. To ensure the long-term consistency of hash codes, especially in incremental learning scenarios, HCFW learns high-level codes derived from category-level semantics. Besides, these codes are adept at handling the category-incremental challenge. 2) Multi-modal Hashing Aspect. HCFW introduces the concept of fine-grained weights designed to facilitate the seamless fusion of complementary multi-modal data, thereby generating multi-modal weights at the instance level and enhancing the overall hashing performance. A comprehensive battery of experiments conducted on two benchmark datasets convincingly underscores the effectiveness and efficiency of HCFW.

cs.MM↗