arXiv subjects

Xiaoxiao Ma

Publications and source records attributed to Xiaoxiao Ma.

At least 55 records · Page 3Linked to original sources

arXiv2025

Enhanced Small Target Detection via Multi-Modal Fusion and Attention Mechanisms: A YOLOv5 Approach

With the rapid development of information technology, modern warfare increasingly relies on intelligence, making small target detection critical in military applications. The growing demand for efficient, real-time detection has created challenges in identifying small targets in complex environments due to interference. To address this, we propose a small target detection method based on multi-modal image fusion and attention mechanisms. This method leverages YOLOv5, integrating infrared and visible light data along with a convolutional attention module to enhance detection performance. The process begins with multi-modal dataset registration using feature point matching, ensuring accurate network training. By combining infrared and visible light features with attention mechanisms, the model improves detection accuracy and robustness. Experimental results on anti-UAV and Visdrone datasets demonstrate the effectiveness and practicality of our approach, achieving superior detection results for small and dim targets.

cs.CV↗

arXiv2025

Stance-Driven Multimodal Controlled Statement Generation: New Dataset and Task

Formulating statements that support diverse or controversial stances on specific topics is vital for platforms that enable user expression, reshape political discourse, and drive social critique and information dissemination. With the rise of Large Language Models (LLMs), controllable text generation towards specific stances has become a promising research area with applications in shaping public opinion and commercial marketing. However, current datasets often focus solely on pure texts, lacking multimodal content and effective context, particularly in the context of stance detection. In this paper, we formally define and study the new problem of stance-driven controllable content generation for tweets with text and images, where given a multimodal post (text and image/video), a model generates a stance-controlled response. To this end, we create the Multimodal Stance Generation Dataset (StanceGen2024), the first resource explicitly designed for multimodal stance-controllable text generation in political discourse. It includes posts and user comments from the 2024 U.S. presidential election, featuring text, images, videos, and stance annotations to explore how multimodal political content shapes stance expression. Furthermore, we propose a Stance-Driven Multimodal Generation (SDMG) framework that integrates weighted fusion of multimodal features and stance guidance to improve semantic consistency and stance control. We release the dataset and code (https://anonymous.4open.science/r/StanceGen-BE9D) for public use and further research.

cs.CL↗

arXiv2025

STAR: Scale-wise Text-conditioned AutoRegressive image generation

We introduce STAR, a text-to-image model that employs a scale-wise auto-regressive paradigm. Unlike VAR, which is constrained to class-conditioned synthesis for images up to 256$\times$256, STAR enables text-driven image generation up to 1024$\times$1024 through three key designs. First, we introduce a pre-trained text encoder to extract and adopt representations for textual constraints, enhancing details and generalizability. Second, given the inherent structural correlation across different scales, we leverage 2D Rotary Positional Encoding (RoPE) and tweak it into a normalized version, ensuring consistent interpretation of relative positions across token maps and stabilizing the training process. Third, we observe that simultaneously sampling all tokens within a single scale can disrupt inter-token relationships, leading to structural instability, particularly in high-resolution generation. To address this, we propose a novel stable sampling method that incorporates causal relationships into the sampling process, ensuring both rich details and stable structures. Compared to previous diffusion models and auto-regressive models, STAR surpasses existing benchmarks in fidelity, text-image consistency, and aesthetic quality, requiring just 2.21s for 1024$\times$1024 images on A100. This highlights the potential of auto-regressive methods in high-quality image synthesis, offering new directions for the text-to-image generation.

cs.CV↗

arXiv2025

Local Detection of Enhanced Hot Electron Scattering in InSb/CdTe Heterostructure Interface

The InSb/CdTe heterojunction structure, characterized by low effective mass and high electron mobility, exhibits interfacial energy band bending, leading to the Rashba spin-orbit coupling effect and nonreciprocal transport, which makes its suitable for spintronic devices with broad applications in logic and storage fields. However, the complex heterojunction interfaces of InSb/CdTe, composed of group III-V and group II-VI semiconductors, are prone to interdiffusion. Therefore, characterization and study of the interfacial properties of InSb/CdTe heterojunctions are crucial for the growth improvement of the InSb/CdTe material system as well as its application in the field of spintronics. In this study, a novel scanning probe microscope, called a scanning noise microscope, was applied to visualize hot electron scattering in InSb/CdTe nano-devices. The results demonstrated that the near-field signal originates from the Coulomb scattering of charged ions on electrons at the interface of the embedded layer heterojunction. This real-space, nondestructive characterization of the heterojunction interface properties offers a new tool for enhancing the performance of heterojunctions.

physics.app-ph↗

arXiv2024

Masked Pre-training Enables Universal Zero-shot Denoiser

In this work, we observe that model trained on vast general images via masking strategy, has been naturally embedded with their distribution knowledge, thus spontaneously attains the underlying potential for strong image denoising. Based on this observation, we propose a novel zero-shot denoising paradigm, i.e., Masked Pre-train then Iterative fill (MPI). MPI first trains model via masking and then employs pre-trained weight for high-quality zero-shot image denoising on a single noisy image. Concretely, MPI comprises two key procedures: 1) Masked Pre-training involves training model to reconstruct massive natural images with random masking for generalizable representations, gathering the potential for valid zero-shot denoising on images with varying noise degradation and even in distinct image types. 2) Iterative filling exploits pre-trained knowledge for effective zero-shot denoising. It iteratively optimizes the image by leveraging pre-trained weights, focusing on alternate reconstruction of different image parts, and gradually assembles fully denoised image within limited number of iterations. Comprehensive experiments across various noisy scenarios underscore the notable advances of MPI over previous approaches with a marked reduction in inference time. Code available at https://github.com/krennic999/MPI.

cs.CV↗

arXiv2024

Data-driven stellar intrinsic colors and dust reddenings for spectro-photometric data: From the blue-edge method to a machine-learning approach

Intrinsic colors (ICs) of stars are essential for the studies on both stellar physics and dust reddening. In this work, we developed an XGBoost model to predict the ICs with the atmospheric parameters $T_{\rm eff}$, ${\rm log}\,g$, and $\rm [M/H]$. The model was trained and tested for three colors at Gaia and 2MASS bands with 1,040,446 low-reddening sources. The atmospheric parameters were determined by the Gaia DR3 GSP-phot module and were validated by comparing with APOGEE and LAMOST. We further confirmed that the biases in GSP-phot parameters, especially for $\rm [M/H]$, do not present a significant impact on the IC prediction. The generalization error of the model estimated by the test set is 0.014 mag for $(G_{\rm BP}\,{-}\,G_{\rm RP})_0$, 0.050 mag for $(G_{\rm BP}\,{-}\,K_{\rm S})_0$, and 0.040 mag for $(J\,{-}\,K_{\rm S})_0$. The model was applied to a sample containing 5,714,528 reddened stars with stellar parameters from Andrae et al. (2023) to calculate ICs and reddenings. The high consistency in the comparison of $E(J\,{-}\,K_{\rm S})$ between our results and literature values further validates the accuracy of the XGBoost model. The variation of $E(G_{\rm BP}\,{-}\,K_{\rm S})/E(G_{\rm BP}\,{-}\,G_{\rm RP})$, a representation of the extinction law, with Galactic longitude is found on large scales. This work preliminarily presents the feasibility and the accuracy of the machine-learning approach for IC and dust reddening calculation, whose products could be widely applied to spectro-photometric data. The data sets and trained model can be accessed via \url{https://doi.org/10.5281/zenodo.12787594}. The models for more bands will be completed in the following works.

astro-ph.SR↗

arXiv2024

Heterogeneous Subgraph Transformer for Fake News Detection

Fake news is pervasive on social media, inflicting substantial harm on public discourse and societal well-being. We investigate the explicit structural information and textual features of news pieces by constructing a heterogeneous graph concerning the relations among news topics, entities, and content. Through our study, we reveal that fake news can be effectively detected in terms of the atypical heterogeneous subgraphs centered on them, which encapsulate the essential semantics and intricate relations between news elements. However, suffering from the heterogeneity, exploring such heterogeneous subgraphs remains an open problem. To bridge the gap, this work proposes a heterogeneous subgraph transformer (HeteroSGT) to exploit subgraphs in our constructed heterogeneous graph. In HeteroSGT, we first employ a pre-trained language model to derive both word-level and sentence-level semantics. Then the random walk with restart (RWR) is applied to extract subgraphs centered on each news, which are further fed to our proposed subgraph Transformer to quantify the authenticity. Extensive experiments on five real-world datasets demonstrate the superior performance of HeteroSGT over five baselines. Further case and ablation studies validate our motivation and demonstrate that performance improvement stems from our specially designed components.

cs.CL↗

arXiv2024

Stronger, Fewer, & Superior: Harnessing Vision Foundation Models for Domain Generalized Semantic Segmentation

In this paper, we first assess and harness various Vision Foundation Models (VFMs) in the context of Domain Generalized Semantic Segmentation (DGSS). Driven by the motivation that Leveraging Stronger pre-trained models and Fewer trainable parameters for Superior generalizability, we introduce a robust fine-tuning approach, namely Rein, to parameter-efficiently harness VFMs for DGSS. Built upon a set of trainable tokens, each linked to distinct instances, Rein precisely refines and forwards the feature maps from each layer to the next layer within the backbone. This process produces diverse refinements for different categories within a single image. With fewer trainable parameters, Rein efficiently fine-tunes VFMs for DGSS tasks, surprisingly surpassing full parameter fine-tuning. Extensive experiments across various settings demonstrate that Rein significantly outperforms state-of-the-art methods. Remarkably, with just an extra 1% of trainable parameters within the frozen backbone, Rein achieves a mIoU of 78.4% on the Cityscapes, without accessing any real urban-scene datasets.Code is available at https://github.com/w1oves/Rein.git.

cs.CV↗

arXiv2024

Seed Optimization with Frozen Generator for Superior Zero-shot Low-light Enhancement

In this work, we observe that the generators, which are pre-trained on massive natural images, inherently hold the promising potential for superior low-light image enhancement against varying scenarios.Specifically, we embed a pre-trained generator to Retinex model to produce reflectance maps with enhanced detail and vividness, thereby recovering features degraded by low-light conditions.Taking one step further, we introduce a novel optimization strategy, which backpropagates the gradients to the input seeds rather than the parameters of the low-light enhancement model, thus intactly retaining the generative knowledge learned from natural images and achieving faster convergence speed. Benefiting from the pre-trained knowledge and seed-optimization strategy, the low-light enhancement model can significantly regularize the realness and fidelity of the enhanced result, thus rapidly generating high-quality images without training on any low-light dataset. Extensive experiments on various benchmarks demonstrate the superiority of the proposed method over numerous state-of-the-art methods qualitatively and quantitatively.

cs.CV↗

arXiv2023

BDEC:Brain Deep Embedded Clustering model

An essential premise for neuroscience brain network analysis is the successful segmentation of the cerebral cortex into functionally homogeneous regions. Resting-state functional magnetic resonance imaging (rs-fMRI), capturing the spontaneous activities of the brain, provides the potential for cortical parcellation. Previous parcellation methods can be roughly categorized into three groups, mainly employing either local gradient, global similarity, or a combination of both. The traditional clustering algorithms, such as "K-means" and "Spectral clustering" may affect the reproducibility or the biological interpretation of parcellations; The region growing-based methods influence the expression of functional homogeneity in the brain at a large scale; The parcellation method based on probabilistic graph models inevitably introduce model assumption biases. In this work, we develop an assumption-free model called as BDEC, which leverages the robust data fitting capability of deep learning. To the best of our knowledge, this is the first study that uses deep learning algorithm for rs-fMRI-based parcellation. By comparing with nine commonly used brain parcellation methods, the BDEC model demonstrates significantly superior performance in various functional homogeneity indicators. Furthermore, it exhibits favorable results in terms of validity, network analysis, task homogeneity, and generalization capability. These results suggest that the BDEC parcellation captures the functional characteristics of the brain and holds promise for future voxel-wise brain network analysis in the dimensionality reduction of fMRI data.

q-bio.NC↗

arXiv2023

Calm local optimality for nonconvex-nonconcave minimax problems

Nonconvex-nonconcave minimax problems have found numerous applications in various fields including machine learning. However, questions remain about what is a good surrogate for local minimax optimum and how to characterize the minimax optimality. Recently Jin, Netrapalli, and Jordan (ICML 2020) introduced a concept of local minimax point and derived optimality conditions for the smooth and unconstrained case. In this paper, we introduce the concept of calm local minimax point, which is a local minimax point with a calm radius function. With the extra calmness property we obtain first and second-order sufficient and necessary optimality conditions for a very general class of nonsmooth nonconvex-nonconcave minimax problem. Moreover we show that the calm local minimax optimality and the local minimax optimality coincide under a weak sufficient optimality condition for the maximization problem. This equivalence allows us to derive stronger optimality conditions under weaker assumptions for local minimax optimality.

math.OC↗

arXiv2023

Automating Method Naming with Context-Aware Prompt-Tuning

Method names are crucial to program comprehension and maintenance. Recently, many approaches have been proposed to automatically recommend method names and detect inconsistent names. Despite promising, their results are still sub-optimal considering the three following drawbacks: 1) These models are mostly trained from scratch, learning two different objectives simultaneously. The misalignment between two objectives will negatively affect training efficiency and model performance. 2) The enclosing class context is not fully exploited, making it difficult to learn the abstract function of the method. 3) Current method name consistency checking methods follow a generate-then-compare process, which restricts the accuracy as they highly rely on the quality of generated names and face difficulty measuring the semantic consistency. In this paper, we propose an approach named AUMENA to AUtomate MEthod NAming tasks with context-aware prompt-tuning. Unlike existing deep learning based approaches, our model first learns the contextualized representation(i.e., class attributes) of PL and NL through the pre-training model, then fully exploits the capacity and knowledge of large language model with prompt-tuning to precisely detect inconsistent method names and recommend more accurate names. To better identify semantically consistent names, we model the method name consistency checking task as a two-class classification problem, avoiding the limitation of previous similarity-based consistency checking approaches. The experimental results reflect that AUMENA scores 68.6%, 72.0%, 73.6%, 84.7% on four datasets of method name recommendation, surpassing the state-of-the-art baseline by 8.5%, 18.4%, 11.0%, 12.0%, respectively. And our approach scores 80.8% accuracy on method name consistency checking, reaching an 5.5% outperformance. All data and trained models are publicly available.

cs.SE↗

arXiv2023

Combined approach with second-order optimality conditions for bilevel programming problems

In this paper, we propose a combined approach with second-order optimality conditions of the lower level problem to study constraint qualifications and optimality conditions for bilevel programming problems. The new method is inspired by the combined approach developed by Ye and Zhu in 2010, where the authors combined the classical first-order and the value function approaches to derive new necessary optimality conditions. In our approach, we add a second-order optimality condition to the combined program as a new constraint. We show that when all known approaches fail, adding the second-order optimality condition as a constraint makes the corresponding partial calmness condition and the resulting necessary optimality condition easier to hold. We also give some discussions on advantages and disadvantages of the combined approaches with the first-order and the second-order information.

math.OC↗

arXiv2022

DAGAD: Data Augmentation for Graph Anomaly Detection

Graph anomaly detection in this paper aims to distinguish abnormal nodes that behave differently from the benign ones accounting for the majority of graph-structured instances. Receiving increasing attention from both academia and industry, yet existing research on this task still suffers from two critical issues when learning informative anomalous behavior from graph data. For one thing, anomalies are usually hard to capture because of their subtle abnormal behavior and the shortage of background knowledge about them, which causes severe anomalous sample scarcity. Meanwhile, the overwhelming majority of objects in real-world graphs are normal, bringing the class imbalance problem as well. To bridge the gaps, this paper devises a novel Data Augmentation-based Graph Anomaly Detection (DAGAD) framework for attributed graphs, equipped with three specially designed modules: 1) an information fusion module employing graph neural network encoders to learn representations, 2) a graph data augmentation module that fertilizes the training set with generated samples, and 3) an imbalance-tailored learning module to discriminate the distributions of the minority (anomalous) and majority (normal) classes. A series of experiments on three datasets prove that DAGAD outperforms ten state-of-the-art baseline detectors concerning various mostly-used metrics, together with an extensive ablation study validating the strength of our proposed modules.

cs.LG↗

arXiv2022

Classical and Machine Learning Interatomic Potentials for BCC Vanadium

BCC transition metals (TMs) exhibit complex temperature and strain-rate dependent plastic deformation behaviour controlled by individual crystal lattice defects. Classical empirical and semi-empirical interatomic potentials have limited capability in modelling defect properties such as the screw dislocation core structures and Peierls barriers in the BCC structure. Machine learning (ML) potentials, trained on DFT-based datasets, have shown some successes in reproducing dislocation core properties. However, in group VB TMs, the most widely-used DFT functionals produce erroneous shear moduli C44 which are undesirably transferred to machine-learning interatomic potentials, leaving current ML approaches unsuitable for this important class of metals and alloys. Here, we develop two interatomic potentials for BCC vanadium (V) based on (i) an extension of the partial electron density and screening parameter in the classical semi-empirical modified embedded-atom method (XMEAM-V) and (ii) a recent hybrid descriptor in the ML Deep Potential framework (DP-HYB-V). We describe distinct features in these two disparate approaches, including their dataset generation, training procedure, weakness and strength in modelling lattice and defect properties in BCC V. Both XMEAM-V and DP-HYB-V reproduce a broad range of defect properties relevant to plastic deformation and fracture. In particular, XMEAM-V reproduces nearly all mechanical and thermodynamic properties at DFT accuracies and with C44 near experimental value. XMEAM-V also naturally exhibits the anomalous slip at 77 K widely observed in group VB and VIB TMs and outperforms all existing, publically available interatomic potentials for V. The XMEAM thus provides a practical path to developing accurate and efficient interatomic potentials for nonmagnetic BCC TMs and possibly multi-principal element TM alloys.

cond-mat.mtrl-sci↗

arXiv2022

A Comprehensive Survey on Graph Anomaly Detection with Deep Learning

Anomalies represent rare observations (e.g., data records or events) that deviate significantly from others. Over several decades, research on anomaly mining has received increasing interests due to the implications of these occurrences in a wide range of disciplines. Anomaly detection, which aims to identify rare observations, is among the most vital tasks in the world, and has shown its power in preventing detrimental events, such as financial fraud, network intrusion, and social spam. The detection task is typically solved by identifying outlying data points in the feature space and inherently overlooks the relational information in real-world data. Graphs have been prevalently used to represent the structural information, which raises the graph anomaly detection problem - identifying anomalous graph objects (i.e., nodes, edges and sub-graphs) in a single graph, or anomalous graphs in a database/set of graphs. However, conventional anomaly detection techniques cannot tackle this problem well because of the complexity of graph data. For the advent of deep learning, graph anomaly detection with deep learning has received a growing attention recently. In this survey, we aim to provide a systematic and comprehensive review of the contemporary deep learning techniques for graph anomaly detection. We compile open-sourced implementations, public datasets, and commonly-used evaluation metrics to provide affluent resources for future studies. More importantly, we highlight twelve extensive future research directions according to our survey results covering unsolved and emerging research problems and real-world applications. With this survey, our goal is to create a "one-stop-shop" that provides a unified understanding of the problem categories and existing approaches, publicly available hands-on resources, and high-impact open challenges for graph anomaly detection using deep learning.

cs.LG↗

arXiv2020

PPO-CMA: Proximal Policy Optimization with Covariance Matrix Adaptation

Proximal Policy Optimization (PPO) is a highly popular model-free reinforcement learning (RL) approach. However, we observe that in a continuous action space, PPO can prematurely shrink the exploration variance, which leads to slow progress and may make the algorithm prone to getting stuck in local optima. Drawing inspiration from CMA-ES, a black-box evolutionary optimization method designed for robustness in similar situations, we propose PPO-CMA, a proximal policy optimization approach that adaptively expands the exploration variance to speed up progress. With only minor changes to PPO, our algorithm considerably improves performance in Roboschool continuous control benchmarks. Our results also show that PPO-CMA, as opposed to PPO, is significantly less sensitive to the choice of hyperparameters, allowing one to use it in complex movement optimization tasks without requiring tedious tuning.

cs.LG↗

arXiv2019

SwitchAgg:A Further Step Towards In-Network Computation

Many distributed applications adopt a partition/aggregation pattern to achieve high performance and scalability. The aggregation process, which usually takes a large portion of the overall execution time, incurs large amount of network traffic and bottlenecks the system performance. To reduce network traffic,some researches take advantage of network devices to commit innetwork aggregation. However, these approaches use either special topology or middle-boxes, which cannot be easily deployed in current datacenters. The emerging programmable RMT switch brings us new opportunities to implement in-network computation task. However, we argue that the architecture of RMT switch is not suitable for in-network aggregation since it is designed primarily for implementing traditional network functions. In this paper, we first give a detailed analysis of in-network aggregation, and point out the key factor that affects the data reduction ratio. We then propose SwitchAgg, which is an innetwork aggregation system that is compatible with current datacenter infrastructures. We also evaluate the performance improvement we have gained from SwitchAgg. Our results show that, SwitchAgg can process data aggregation tasks at line rate and gives a high data reduction rate, which helps us to cut down network traffic and alleviate pressure on server CPU. In the system performance test, the job-completion-time can be reduced as much as 50%

cs.DC↗