arXiv subjects

Xiangyu Huang

Publications and source records attributed to Xiangyu Huang.

10 recordsLinked to original sources

arXiv2026

RT-VLA: Real-Time Vision-Language-Action Models via Knowledge Distillation

Vision-Language-Action (VLA) models have shown strong potential for end-to-end autonomous driving by jointly modeling visual perception, language reasoning, explainability and action prediction. However, their large vision-language backbones and reasoning modules introduce substantial inference latency and thereby prevent their deployment in the unforgiving reality of the road networks. We propose RT-VLA, a lightweight, distilled VLA model that transfers the driving and reasoning capabilities of the state-of-the-art SimLingo model into a compact student through multi-level supervised distillation. RT-VLA preserves language-based reasoning and supports post-hoc explanation through offline language analysis of safety-critical driving moments without adding latency to real-time control. Compared to the SimLingo teacher, RT-VLA maintains competitive closed-loop driving and language reasoning performance while reducing inference time by 44.8X in vision-only mode and 7.9X in vision+language mode. These results suggest that supervised distillation is a practical approach for building real-time, explainable VLA-style autonomous driving models.

cs.CV↗

arXiv2026

Large deviation principle for empirical measures of once-reinforced random walks on finite graphs

A $δ$ once-reinforced random walk ($δ$-ORRW) on connected graph is a self-interacting random walk which moves to its neighbors at each step according to the weights of the edges at that time, where the weights are $1$ on edges that have not been traversed and $δ$ otherwise. In this paper, we prove a large deviation principle for empirical measures of $δ$-ORRWs on finite connected graphs using a modified weak convergence approach. The rate function of the large deviation principle exhibits a phase transition at the $δ=1$.

math.PR↗

arXiv2025

Lens Model Accuracy in the Expected LSST Lensed AGN Sample

Strong gravitational lensing of active galactic nuclei (AGN) enables measurements of cosmological parameters through time-delay cosmography (TDC). With data from the upcoming LSST survey, we anticipate using a sample of O(1000) lensed AGN for TDC. To prepare for this dataset and enable this measurement, we construct and analyze a realistic mock sample of 1300 systems drawn from the OM10 (Oguri & Marshall 2010) catalog of simulated lenses with AGN sources at $z<3.1$ in order to test a key aspect of the analysis pipeline, that of the lens modeling. We realize the lenses as power law elliptical mass distributions and simulate 5-year LSST i-band coadd images. From every image, we infer the lens mass model parameters using neural posterior estimation (NPE). Focusing on the key model parameters, $θ_E$ (the Einstein Radius) and $γ_{lens}$ (the projected mass density profile slope), with consistent mass-light ellipticity correlations in test and training data, we recover $θ_E$ with less than 1% bias per lens, 6.5% precision per lens and $γ_{lens}$ with less than 3% bias per lens, 8% precision per lens. We find that lens light subtraction prior to modeling is only useful when applied to data sampled from the training prior. If emulated deconvolution is applied to the data prior to modeling, precision improves across all parameters by a factor of 2. Finally, we combine the inferred lens mass models using Bayesian Hierarchical Inference to recover the global properties of the lens sample with less than 1% bias.

astro-ph.CO↗

arXiv2025

UniOcc: A Unified Benchmark for Occupancy Forecasting and Prediction in Autonomous Driving

We introduce UniOcc, a comprehensive, unified benchmark and toolkit for occupancy forecasting (i.e., predicting future occupancies based on historical information) and occupancy prediction (i.e., predicting current-frame occupancy from camera images. UniOcc unifies the data from multiple real-world datasets (i.e., nuScenes, Waymo) and high-fidelity driving simulators (i.e., CARLA, OpenCOOD), providing 2D/3D occupancy labels and annotating innovative per-voxel flows. Unlike existing studies that rely on suboptimal pseudo labels for evaluation, UniOcc incorporates novel evaluation metrics that do not depend on ground-truth labels, enabling robust assessment on additional aspects of occupancy quality. Through extensive experiments on state-of-the-art models, we demonstrate that large-scale, diverse training data and explicit flow information significantly enhance occupancy prediction and forecasting performance. Our data and code are available at https://uniocc.github.io/.

cs.CV↗

arXiv2025

Tail distributions of cover times of once-reinforced random walks

We consider the tail distribution of the edge cover time of a specific non-Markov process, $δ$ once-reinforced random walk, on finite connected graphs, whose transition probability is proportional to weights of edges. Here the weights are $1$ on edges not traversed and $δ\in(0,\infty)$ otherwise. In detail, we show that its tail distribution decays exponentially, and obtain a phase transition of the exponential integrability of the edge cover time with critical exponent $α_c^1(δ)$, which has a variational representation and some interesting analytic properties including $α_c^1(0+)$ reflecting the graph structures.

math.PR↗

arXiv2023

On Negative Correlation of Arboreal Gas on Some Graphs

Arboreal Gas is a type of (unrooted) random forest on a graph, where the probability is determined by a parameter $β>0$ per edge. This model is essentially equivalent to acyclic Bernoulli bond percolation with a parameter $p=β/(1+β)$. Additionally, Arboreal Gas can be considered as the limit of the $q$-states random cluster model with $p=βq$ as $q\to 0$. A natural question arises regarding the existence and performance of the weak limit of Arboreal Gas as the graph size goes to infinity. The answer to this question relies on the negative correlation of Arboreal Gas, which is still an open problem. This paper primarily focuses on the negative correlation of Arboreal Gas and provides some results for specific graphs.

math.PR↗

arXiv2023

Updated version: A Video Anomaly Detection Framework based on Appearance-Motion Semantics Representation Consistency

Video anomaly detection is an essential but challenging task. The prevalent methods mainly investigate the reconstruction difference between normal and abnormal patterns but ignore the semantics consistency between appearance and motion information of behavior patterns, making the results highly dependent on the local context of frame sequences and lacking the understanding of behavior semantics. To address this issue, we propose a framework of Appearance-Motion Semantics Representation Consistency that uses the gap of appearance and motion semantic representation consistency between normal and abnormal data. The two-stream structure is designed to encode the appearance and motion information representation of normal samples, and a novel consistency loss is proposed to enhance the consistency of feature semantics so that anomalies with low consistency can be identified. Moreover, the lower consistency features of anomalies can be used to deteriorate the quality of the predicted frame, which makes anomalies easier to spot. Experimental results demonstrate the effectiveness of the proposed method.

cs.CV↗

arXiv2023

Synthetic Pseudo Anomalies for Unsupervised Video Anomaly Detection: A Simple yet Efficient Framework based on Masked Autoencoder

Due to the limited availability of anomalous samples for training, video anomaly detection is commonly viewed as a one-class classification problem. Many prevalent methods investigate the reconstruction difference produced by AutoEncoders (AEs) under the assumption that the AEs would reconstruct the normal data well while reconstructing anomalies poorly. However, even with only normal data training, the AEs often reconstruct anomalies well, which depletes their anomaly detection performance. To alleviate this issue, we propose a simple yet efficient framework for video anomaly detection. The pseudo anomaly samples are introduced, which are synthesized from only normal data by embedding random mask tokens without extra data processing. We also propose a normalcy consistency training strategy that encourages the AEs to better learn the regular knowledge from normal and corresponding pseudo anomaly data. This way, the AEs learn more distinct reconstruction boundaries between normal and abnormal data, resulting in superior anomaly discrimination capability. Experimental results demonstrate the effectiveness of the proposed method.

cs.CV↗

arXiv2023

Multi-level Memory-augmented Appearance-Motion Correspondence Framework for Video Anomaly Detection

Frame prediction based on AutoEncoder plays a significant role in unsupervised video anomaly detection. Ideally, the models trained on the normal data could generate larger prediction errors of anomalies. However, the correlation between appearance and motion information is underutilized, which makes the models lack an understanding of normal patterns. Moreover, the models do not work well due to the uncontrollable generalizability of deep AutoEncoder. To tackle these problems, we propose a multi-level memory-augmented appearance-motion correspondence framework. The latent correspondence between appearance and motion is explored via appearance-motion semantics alignment and semantics replacement training. Besides, we also introduce a Memory-Guided Suppression Module, which utilizes the difference from normal prototype features to suppress the reconstruction capacity caused by skip-connection, achieving the tradeoff between the good reconstruction of normal data and the poor reconstruction of abnormal data. Experimental results show that our framework outperforms the state-of-the-art methods, achieving AUCs of 99.6\%, 93.8\%, and 76.3\% on UCSD Ped2, CUHK Avenue, and ShanghaiTech datasets.

cs.CV↗

arXiv2022

A Video Anomaly Detection Framework based on Appearance-Motion Semantics Representation Consistency

Video anomaly detection refers to the identification of events that deviate from the expected behavior. Due to the lack of anomalous samples in training, video anomaly detection becomes a very challenging task. Existing methods almost follow a reconstruction or future frame prediction mode. However, these methods ignore the consistency between appearance and motion information of samples, which limits their anomaly detection performance. Anomalies only occur in the moving foreground of surveillance videos, so the semantics expressed by video frame sequences and optical flow without background information in anomaly detection should be highly consistent and significant for anomaly detection. Based on this idea, we propose Appearance-Motion Semantics Representation Consistency (AMSRC), a framework that uses normal data's appearance and motion semantic representation consistency to handle anomaly detection. Firstly, we design a two-stream encoder to encode the appearance and motion information representations of normal samples and introduce constraints to further enhance the consistency of the feature semantics between appearance and motion information of normal samples so that abnormal samples with low consistency appearance and motion feature representation can be identified. Moreover, the lower consistency of appearance and motion features of anomalous samples can be used to generate predicted frames with larger reconstruction error, which makes anomalies easier to spot. Experimental results demonstrate the effectiveness of the proposed method.

cs.CV↗