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Dong Wang

Publications and source records attributed to Dong Wang.

At least 127 records · Page 7Linked to original sources
arXiv2026

UAV-DETR: DETR for Anti-Drone Target Detection

Drone detection is pivotal in numerous security and counter-UAV applications. However, existing deep learning-based methods typically struggle to balance robust feature representation with computational efficiency. This challenge is particularly acute when detecting miniature drones against complex backgrounds under severe environmental interference. To address these issues, we introduce UAV-DETR, a novel framework that integrates a small-target-friendly architecture with real-time detection capabilities. Specifically, UAV-DETR features a WTConv-enhanced backbone and a Sliding Window Self-Attention (SWSA-IFI) encoder, capturing the high-frequency structural details of tiny targets while drastically reducing parameter overhead. Furthermore, we propose an Efficient Cross-Scale Feature Recalibration and Fusion Network (ECFRFN) to suppress background noise and aggregate multi-scale semantics. To further enhance accuracy, UAV-DETR incorporates a hybrid Inner-CIoU and NWD loss strategy, mitigating the extreme sensitivity of standard IoU metrics to minor positional deviations in small objects. Extensive experiments demonstrate that UAV-DETR significantly outperforms the baseline RT-DETR on our custom UAV dataset (+6.61% in mAP50:95, with a 39.8% reduction in parameters) and the public DUT-ANTI-UAV benchmark (+1.4% in Precision, +1.0% in F1-Score). These results establish UAV-DETR as a superior trade-off between efficiency and precision in counter-UAV object detection. The code is available at https://github.com/wd-sir/UAVDETR.

cs.CV↗
arXiv2026

Edge Radar Material Classification Under Geometry Shifts

Material awareness can improve robotic navigation and interaction, particularly in conditions where cameras and LiDAR degrade. We present a lightweight mmWave radar material classification pipeline designed for ultra-low-power edge devices (TI IWRL6432), using compact range-bin intensity descriptors and a Multilayer Perceptron (MLP) for real-time inference. While the classifier reaches a macro-F1 of 94.2\% under the nominal training geometry, we observe a pronounced performance drop under realistic geometry shifts, including sensor height changes and small tilt angles. These perturbations induce systematic intensity scaling and angle-dependent radar cross section (RCS) effects, pushing features out of distribution and reducing macro-F1 to around 68.5\%. We analyze these failure modes and outline practical directions for improving robustness with normalization, geometry augmentation, and motion-aware features.

cs.RO↗
arXiv2026

Efficient Diffusion as Low Light Enhancer

The computational burden of the iterative sampling process remains a major challenge in diffusion-based Low-Light Image Enhancement (LLIE). Current acceleration methods, whether training-based or training-free, often lead to significant performance degradation, highlighting the trade-off between performance and efficiency. In this paper, we identify two primary factors contributing to performance degradation: fitting errors and the inference gap. Our key insight is that fitting errors can be mitigated by linearly extrapolating the incorrect score functions, while the inference gap can be reduced by shifting the Gaussian flow to a reflectance-aware residual space. Based on the above insights, we design Reflectance-Aware Trajectory Refinement (RATR) module, a simple yet effective module to refine the teacher trajectory using the reflectance component of images. Following this, we introduce \textbf{Re}flectance-aware \textbf{D}iffusion with \textbf{Di}stilled \textbf{T}rajectory (\textbf{ReDDiT}), an efficient and flexible distillation framework tailored for LLIE. Our framework achieves comparable performance to previous diffusion-based methods with redundant steps in just 2 steps while establishing new state-of-the-art (SOTA) results with 8 or 4 steps. Comprehensive experimental evaluations on 10 benchmark datasets validate the effectiveness of our method, consistently outperforming existing SOTA methods.

cs.CV↗
arXiv2026

Dynamic-ICP: Doppler-Aware Iterative Closest Point Registration for Dynamic Scenes

Reliable odometry in highly dynamic environments remains challenging when it relies on ICP-based registration: ICP assumes near-static scenes and degrades in repetitive or low-texture geometry. We introduce Dynamic-ICP, a Doppler-aware registration framework. The method (i) estimates ego motion from per-point Doppler velocity via robust regression and builds a velocity filter, (ii) clusters dynamic objects and reconstructs object-wise translational velocities from ego-compensated radial measurements, (iii) predicts dynamic points with a constant-velocity model, and (iv) aligns scans using a compact objective that combines point-to-plane geometry residual with a translation-invariant, rotation-only Doppler residual. The approach requires no external sensors or sensor-vehicle calibration and operates directly on FMCW LiDAR range and Doppler velocities. We evaluate Dynamic-ICP on three datasets-HeRCULES, HeLiPR, AevaScenes-focusing on highly dynamic scenes. Dynamic-ICP consistently improves rotational stability and translation accuracy over the state-of-the-art methods. Our approach is also simple to integrate into existing pipelines, runs in real time, and provides a lightweight solution for robust registration in dynamic environments. To encourage further research, the code is available at: https://github.com/JMUWRobotics/Dynamic-ICP.

cs.RO↗
arXiv2026

FreeGaussian: Annotation-free Control of Articulated Objects via 3D Gaussian Splats with Flow Derivatives

Reconstructing controllable Gaussian splats for articulated objects from monocular video is especially challenging due to its inherently insufficient constraints. Existing methods address this by relying on dense masks and manually defined control signals, limiting their real-world applications. In this paper, we propose an annotation-free method, FreeGaussian, which mathematically disentangles camera egomotion and articulated movements via flow derivatives. By establishing a connection between 2D flows and 3D Gaussian dynamic flow, our method enables optimization and continuity of dynamic Gaussian motions from flow priors without any control signals. Furthermore, we introduce a 3D spherical vector controlling scheme, which represents the state as a 3D Gaussian trajectory, thereby eliminating the need for complex 1D control signal calculations and simplifying controllable Gaussian modeling. Extensive experiments on articulated objects demonstrate the state-of-the-art visual performance and precise, part-aware controllability of our method. Code is available at: https://github.com/Tavish9/freegaussian.

cs.CV↗
arXiv2026

Open-Vocabulary Octree-Graph for 3D Scene Understanding

Open-vocabulary 3D scene understanding is indispensable for embodied agents. Recent works leverage pretrained vision-language models (VLMs) for object segmentation and project them to point clouds to build 3D maps. Despite progress, a point cloud is a set of unordered coordinates that requires substantial storage space and does not directly convey occupancy information or spatial relation, making existing methods inefficient for downstream tasks, e.g., path planning and text-based object retrieval. To address these issues, we propose \textbf{Octree-Graph}, a novel scene representation for open-vocabulary 3D scene understanding. Specifically, a Chronological Group-wise Segment Merging (CGSM) strategy and an Instance Feature Aggregation (IFA) algorithm are first designed to get 3D instances and corresponding semantic features. Subsequently, an adaptive-octree structure is developed that stores semantics and depicts the occupancy of an object adjustably according to its shape. Finally, the Octree-Graph is constructed where each adaptive-octree acts as a graph node, and edges describe the spatial relations among nodes. Extensive experiments on various tasks are conducted on several widely-used datasets, demonstrating the versatility and effectiveness of our method. Code is available \href{https://github.com/yifeisu/OV-Octree-Graph}{here}.

cs.CV↗
arXiv2026

Closed-Loop Action Chunks with Dynamic Corrections for Training-Free Diffusion Policy

Diffusion-based policies have achieved remarkable results in robotic manipulation but often struggle to adapt rapidly in dynamic scenarios, leading to delayed responses or task failures. We present DCDP, a Dynamic Closed-Loop Diffusion Policy framework that integrates chunk-based action generation with real-time correction. DCDP integrates a self-supervised dynamic feature encoder, cross-attention fusion, and an asymmetric action encoder-decoder to inject environmental dynamics before action execution, achieving real-time closed-loop action correction and enhancing the system's adaptability in dynamic scenarios. In dynamic PushT simulations, DCDP improves adaptability by 19\% without retraining while requiring only 5\% additional computation. Its modular design enables plug-and-play integration, achieving both temporal coherence and real-time responsiveness in dynamic robotic scenarios, including real-world manipulation tasks. The project page is at: https://github.com/wupengyuan/dcdp

cs.RO↗
arXiv2026

Learning 2D Invariant Affordance Knowledge for 3D Affordance Grounding

3D Object Affordance Grounding aims to predict the functional regions on a 3D object and has laid the foundation for a wide range of applications in robotics. Recent advances tackle this problem via learning a mapping between 3D regions and a single human-object interaction image. However, the geometric structure of the 3D object and the object in the human-object interaction image are not always consistent, leading to poor generalization. To address this issue, we propose to learn generalizable invariant affordance knowledge from multiple human-object interaction images within the same affordance category. Specifically, we introduce the Multi-Image Guided Invariant-Feature-Aware 3D Affordance Grounding (MIFAG) framework. It grounds 3D object affordance regions by identifying common interaction patterns across multiple human-object interaction images. First, the Invariant Affordance Knowledge Extraction Module (IAM) utilizes an iterative updating strategy to gradually extract aligned affordance knowledge from multiple images and integrate it into an affordance dictionary. Then, the Affordance Dictionary Adaptive Fusion Module (ADM) learns comprehensive point cloud representations that consider all affordance candidates in multiple images. Besides, the Multi-Image and Point Affordance (MIPA) benchmark is constructed and our method outperforms existing state-of-the-art methods on various experimental comparisons.

cs.CV↗
arXiv2026

Design of Angular-offset Interstitial-Tube- Assisted Hollow-Core Fibers with Ultrahigh Mode Purity and Ultralow Loss

Antiresonant hollow-core fibres (AR-HCFs) have recently reached attenuation far below the Rayleigh-scattering limit of silica, but their inherently multimode nature remains a major challenge for practical systems requiring high modal purity. In particular, suppressing higher-order modes (HOMs) at the 1 dB/m level while maintaining sub-0.1 dB/km fundamental-mode (FM) loss is difficult because conventional filtering strategies rely on tuning nested-tube dimensions, a design freedom that becomes increasingly restricted in the ultralow-loss regime. Here, we propose a new HOM-control mechanism in an interstitial-tube-assisted double nested anti-resonant nodeless fiber (IT-DNANF) by introducing angular offset of the interstitial tubes. Instead of using nested cavities as the primary tuning element, the proposed approach exploits the gap region between adjacent cladding tubes as a leakage-adjacent modal-control interface. Numerical simulations show that the offset increases both FM and HOM losses, but with a substantially stronger sensitivity for HOMs, leading to rapid enhancement of differential modal loss. Furthermore, when the gap-region FM is tuned into phase matching with the core HOM, strong coupling to a high-leakage state is induced, resulting in a pronounced HOM-loss peak. Using the practical criterion of HOM losslarger than 1 dB/m, we identify optimized IT-DNANF designs that achieve rapid HOM stripping while maintaining FM loss below 0.05 dB/km at 1550 nm. This work establishes angular offset as a physically distinct and manufacturability-friendly degree of freedom for mode purification in ultralow-loss hollow-core fibres.

physics.optics↗
arXiv2026

PriorIDENT: Prior-Informed PDE Identification from Noisy Data

Identifying governing partial differential equations (PDEs) from noisy spatiotemporal data remains challenging due to differentiation-induced noise amplification and ambiguity from overcomplete libraries. We propose a prior-informed weak-form sparse-regression framework that resolves both issues by refining the dictionary before regression and shifting derivatives onto smooth test functions. Our design encodes three compact physics priors-Hamiltonian (skew-gradient and energy-conserving), conservation-law (flux-form with shared cross-directional coefficients), and energy-minimization (variational, dissipative)-so that all candidate features are physically admissible by construction. These prior-consistent libraries are coupled with a subspace-pursuit pipeline enhanced by trimming and residual-reduction model selection to yield parsimonious, interpretable models. Across canonical systems-including Hamiltonian oscillators and the three-body problem, viscous Burgers and two-dimensional shallow-water equations, and diffusion and Allen--Cahn dynamics-our method achieves higher true-positive rates, stable coefficient recovery, and structure-preserving dynamics under substantial noise, consistently outperforming no-prior baselines in both strong- and weak-form settings. The results demonstrate that compact structural priors, when combined with weak formulations, provide a robust and unified route to physically faithful PDE identification from noisy data.

math.NA↗
arXiv2026

Training-Free Multi-Step Inference for Target Speaker Extraction

Target speaker extraction (TSE) aims to recover a target speaker's speech from a mixture using a reference utterance as a cue. Most TSE systems adopt conditional auto-encoder architectures with one-step inference. Inspired by test-time scaling, we propose a training-free multi-step inference method that enables iterative refinement with a frozen pretrained model. At each step, new candidates are generated by interpolating the original mixture and the previous estimate, and the best candidate is selected for further refinement until convergence. Experiments show that, when ground-truth target speech is available, optimizing an intrusive metric (SI-SDRi) yields consistent gains across multiple evaluation metrics. Without ground truth, optimizing non-intrusive metrics (UTMOS or SpkSim) improves the corresponding metric but may hurt others. We therefore introduce joint metric optimization to balance these objectives, enabling controllable extraction preferences for practical deployment.

cs.SD↗
arXiv2026

Evaluating Large Language Models for Multilingual Vulnerability Detection at Dual Granularities

Various deep learning-based approaches utilizing pre-trained language models (PLMs) have been proposed for automated vulnerability detection. With recent advancements in large language models (LLMs), several studies have begun exploring their application to vulnerability detection tasks. However, existing studies primarily focus on specific programming languages (e.g., C/C++) and function-level detection, leaving the strengths and weaknesses of PLMs and LLMs in multilingual and multi-granularity scenarios largely unexplored. To bridge this gap, we conduct a comprehensive fine-grained empirical study evaluating the effectiveness of state-of-the-art PLMs and LLMs for multilingual vulnerability detection. Using over 30,000 real-world vulnerability-fixing patches across seven programming languages, we systematically assess model performance at both the function-level and line-level. Our key findings indicate that GPT-4o, enhanced through instruction tuning and few-shot prompting, significantly outperforms all other evaluated models, including CodeT5P. Furthermore, the LLM-based approach demonstrates superior capability in detecting unique multilingual vulnerabilities, particularly excelling in identifying the most dangerous and high-severity vulnerabilities. These results underscore the promising potential of adopting LLMs for multilingual vulnerability detection at function-level and line-level, revealing their complementary strengths and substantial improvements over PLM approaches. This empirical evaluation of PLMs and LLMs for multilingual vulnerability detection highlights LLMs' value in addressing real-world software security challenges.

cs.SE↗
arXiv2026

From Spatial to Actions: Grounding Vision-Language-Action Model in Spatial Foundation Priors

Existing vision-language-action (VLA) models act in 3D real-world but are typically built on 2D encoders, leaving a spatial reasoning gap that limits generalization and adaptability. Recent 3D integration techniques for VLAs either require specialized sensors and transfer poorly across modalities, or inject weak cues that lack geometry and degrade vision-language alignment. In this work, we introduce FALCON (From Spatial to Action), a novel paradigm that injects rich 3D spatial tokens into the action head. FALCON leverages spatial foundation models to deliver strong geometric priors from RGB alone, and includes an Embodied Spatial Model that can optionally fuse depth, or pose for higher fidelity when available, without retraining or architectural changes. To preserve language reasoning, spatial tokens are consumed by a Spatial-Enhanced Action Head rather than being concatenated into the vision-language backbone. These designs enable FALCON to address limitations in spatial representation, modality transferability, and alignment. In comprehensive evaluations across three simulation benchmarks and eleven real-world tasks, our proposed FALCON achieves state-of-the-art performance, consistently surpasses competitive baselines, and remains robust under clutter, spatial-prompt conditioning, and variations in object scale and height.

cs.RO↗
arXiv2026

Straintronics and twistronics in bilayer graphene

The interplay of twist and strain in bilayer graphene enables the formation of moiré patterns and narrow bands that host correlated and topological phases. While magic-angle twisted bilayer graphene has been widely studied, strain provides an additional and realistic control knob for band engineering. In this work, we first generate a global method to construct commensurate supercells for arbitrary twist and heterostrain. Then, using atomistic tight-binding and strain-extended continuum models to study the commensurate structures, we identify configurations that minimize the bandwidth beyond the magic angle. The results reveal a strong dependence of band narrowing and topology on strain type, magnitude, direction and lattice relaxation. Particularly, shear strain produces a stronger distortion than uniaxial strain. Including electron-electron interactions through a self-consistent Hartree potential shows that strain broadens the bare bands while reducing electrostatic renormalization. Strain also drives topological transitions as the narrow and remote bands hybridize, establishing twisted and strained bilayer graphene as a tunable platform for flat-band and topological phenomena.

cond-mat.mes-hall↗
arXiv2026

Quantum steering as a probe of energy transfer in quantum batteries

This study investigates the role of EPR steering in characterizing the energy dynamics of quantum batteries (QBs) within \textcolor{black}{a charging system that features shared reservoirs. After optimizing parameter configurations to achieve high-energy systems, we observe across a variety of charging scenarios with low-dissipation regimes that steering serves as a vital resource: it is initially stored until the system reaches energy equilibrium, and then subsequently utilized to sustain the enhancement of energy storage. Furthermore, steering acts as a witness to battery population balance and a consumable that enhances extractable work. Additionally, we discuss the contribution of the steering potential to energy upon high-dissipation charging in details. These findings establish a novel indicator for monitoring QB energy variations, which will be beneficial to achieve the high-performance quantum batteries.

quant-ph↗
arXiv2026

SAGA: Selective Adaptive Gating for Efficient and Expressive Linear Attention

While Transformer architecture excel at modeling long-range dependencies contributing to its widespread adoption in vision tasks the quadratic complexity of softmax-based attention mechanisms imposes a major bottleneck, particularly when processing high-resolution images. Linear attention presents a promising alternative by reformulating the attention computation from $(QK)V$ to $Q(KV)$, thereby reducing the complexity from $\mathcal{O}(N^2)$ to $\mathcal{O}(N)$ while preserving the global receptive field. However, most existing methods compress historical key-value (KV) information uniformly, which can lead to feature redundancy and the loss of directional alignment with the query (Q). This uniform compression results in low-rank $KV$ feature maps, contributing to a performance gap compared to softmax attention. To mitigate this limitation, we propose \textbf{S}elective \textbf{A}daptive \textbf{GA}ting for Efficient and Expressive Linear Attention (SAGA) , which introduces input-adaptive learnable gates to selectively modulate information aggregation into the $KV$ feature map. These gates enhance semantic diversity and alleviate the low-rank constraint inherent in conventional linear attention. Additionally, we propose an efficient Hadamard-product decomposition method for gate computation, which introduces no additional memory overhead. Experiments demonstrate that SAGA achieves a 1.76$\times$ improvement in throughput and a 2.69$\times$ reduction in peak GPU memory compared to PVT-T at a resolution of $1280 \times 1280$. Moreover, it improves top-1 accuracy by up to 4.4\% on the ImageNet dataset, demonstrating both computational efficiency and model effectiveness.

cs.CV↗
arXiv2026

Robust coherent control in non-Hermitian cavity electromagnonics using counterdiabatic driving

We propose to use counterdiabatic driving (CD) shortcut and the Floquet engineering to realize the robust and fast state transfer in the dissipation cavity magnon-polaritons non-Hermitian (NH) system. For the two-level NH cavity magnon-polaritons Hamiltonian, an accurate and fast population transfer is achieved from the microwave photon to the magnon by two coherent control techniques; counterdiabatic driving shortcut and non-Hermitian shortcuts (NHSs). Additionally, by using the CD technique, the population evolution speed of non-Hermitian systems is faster than that via the NHS technique in the broken-symmetric regime. Furthermore, we compare their performances in the presence of the coupling strength and systematic errors, the CD technique features a broad range of high efficiencies of the transition probability above 99.9%, showing that the CD technique is more robustness against these errors than the NHS technique. It is worth noting that this advantage becomes more significant as the gain rate of system parameters increases. The work provides a basis for achieving the robust coherent control in NH cavity electromagnonics.

quant-ph↗
arXiv2026

Robust composite two-qubit gates for silicon-based spin qubits

We propose a universal approach based on Hamiltonian inverse engineering to realize a set of parameterized two-qubit gates. This method possesses unique advantages to simultaneous control of transitions among four energy levels, providing a simpler and effective way to construct composite two-qubit gates with fewer operations than traditional methods. Applied to silicon double quantum dots (DQDs), one can realize a one-step fSim gate and a B gate with only one pulse switch. Of note, the method can be further integrated with various optimization theories to enhance gate performance. Based on quantum optimal control theory, we develop a high-fidelity fSim gate scheme with experimentally feasible pulse shapes, featuring an average gate time of 50 ns and a theoretical fidelity of 99.95% in the presence of decoherence and approximation error. By incorporating geometric quantum gate principles, we propose a combined geometric and dynamic fSim gate scheme. Numerical simulations demonstrate that this hybrid scheme exhibits stronger robustness against systematic errors compared to the purely dynamic approach. Our method is generalizable to arbitrary two-qubit physical systems, offering a feasible pathway for rapidly and robustly constructing composite two-qubit gates.

quant-ph↗