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新论文 · v1arXiv:2611.14947

One Block Is Enough: LoopMR for Accelerated MRI Reconstruction

Yimeng He, Yibin Xie, Yi Zheng, Yifan Gao, Lixia Wang, Yin-Chen Hsu, Yufeng Wang, Meng Lu 等 10 位作者

原文摘要

Deep-learning unrolled networks are the leading approach to accelerated MRI reconstruction. State-of-the-art models such as PromptMR+ learn a distinct block for every cascade, so their size grows with the number of cascades. Sharing one block across iterations avoids adding parameters, but earlier shared-weight models typically used a tiny block and produced worse images than models with distinct weights. Therefore, it remained unclear whether weight sharing itself or the choice of block was the problem. Recent advances in looped transformers, which reuse the same block across layers, show that they can match or outperform their distinct-weight counterparts on several tasks. Motivated by this, we propose LoopMR, which reuses a single Restormer block at every iteration as its core. Besides the current image estimate, the block receives data-consistency inputs and latent images. On four datasets covering the brain (CC359), knee (fastMRI), heart (CMRxRecon2023) and carotid (in-house), LoopMR significantly outperforms E2E-VarNet, PromptMR and PromptMR+ in PSNR and SSIM at every acceleration factor (p < 0.01), with about 1/9 of PromptMR+'s parameters (10.3M versus 92.9M). It also keeps its lead at unseen acceleration factors and on an unseen dataset. With our transformer block, the shared model matches or outperforms its distinct-weight counterpart at 6, 12 and 24 iterations. At a fixed number of iterations, shared transformer blocks match or outperform their distinct-weight versions, while shared CNN blocks perform worse. These results suggest that looping one well-chosen block can outperform stacking distinct cascades in MRI reconstruction, with a fraction of the parameters.

eess.IV · cs.CV

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arXiv ↗PDF ↗
版本更新 · v4arXiv:2602.16681

VETime: Vision Enhanced Zero-Shot Time Series Anomaly Detection

Yingyuan Yang, Tian Lan, Yifei Gao, Yimeng Lu, Xuming An, Wenjun He, Meng Wang, Chenghao Liu 等 9 位作者

原文摘要

Time-series anomaly detection (TSAD) requires identifying both immediate Point Anomalies and long-range Context Anomalies. However, existing foundation models face a fundamental trade-off: 1D temporal models provide fine-grained pointwise localization but lack a global contextual perspective, while 2D vision-based models capture global patterns but suffer from information bottlenecks due to a lack of temporal alignment and coarse-grained pointwise detection. To resolve this dilemma, we propose VETime, the first TSAD framework that unifies temporal and visual modalities through fine-grained visual-temporal alignment and dynamic fusion. VETime introduces a Reversible Image Conversion and a Patch-Level Temporal Alignment module to establish a shared visual-temporal timeline, preserving discriminative details while maintaining temporal sensitivity. Furthermore, we design an Anomaly Window Contrastive Learning mechanism and a Task-Adaptive Multi-Modal Fusion to adaptively integrate the complementary perceptual strengths of both modalities. Extensive experiments demonstrate that VETime significantly outperforms state-of-the-art models in zero-shot scenarios, achieving superior localization precision with lower computational overhead than current vision-based approaches. Code available at: https://github.com/yyyangcoder/VETime .

cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.06583

PatchCon: Patch-aware Weighted Supervised Contrastive Learning for Vulnerability Detection

Chaomeng Lu, Pooya Rostami Mazrae, Bert Lagaisse

原文摘要

Automated vulnerability detection is critical for securing modern software systems, yet existing learning-based approaches often treat functions as independent samples and underuse the supervision provided by vulnerability-fixing patches. In this paper, we propose PatchCon, a CodeBERT-based patch-aware contrastive learning model for function-level vulnerability detection. PatchCon jointly optimizes vulnerability classification and supervised contrastive learning, and treats vulnerable-patched function pairs as hard negatives with larger contrastive weights to capture subtle security-relevant differences introduced by fixes. To evaluate patch-level discrimination, we use two pairwise metrics that measure whether a model can correctly distinguish vulnerable functions from their patched counterparts. We evaluate PatchCon on PairVul-Linux through extensive experiments, including state-of-the-art comparisons, ablation studies, hyperparameter sensitivity analysis, and statistical significance testing. We also conduct fine-grained analyses by Common Weakness Enumeration (CWE) type, function length, and patch size, and further evaluate PatchCon on Linux kernel vulnerabilities disclosed after the training data cutoff. On PairVul-Linux, PatchCon outperforms representative pretrained code models and contrastive learning baselines on both classification and pairwise metrics, improves pairwise accuracy over the strongest reproduced baseline from 0.2060 to 0.3264, and achieves pairwise accuracy comparable to that of a GPT-4o-based large language model (LLM) method at a substantially lower cost. Among the reproduced models, PatchCon also achieves the highest pairwise accuracy on test pairs from Common Vulnerabilities and Exposures (CVE) entries unseen during training and on newly disclosed vulnerabilities.

cs.SE

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arXiv ↗PDF ↗
版本更新 · v4arXiv:2512.10485

From Lab to Reality: A Practical Evaluation of Deep Learning Models and LLMs for Vulnerability Detection

Chaomeng Lu, Bert Lagaisse

原文摘要

Vulnerability detection methods based on deep learning (DL) have shown strong performance on benchmark datasets, yet their real-world effectiveness remains underexplored. Recent work suggests that graph neural network-based and transformer-based models, including large language models (LLMs), yield promising results when evaluated on curated benchmark datasets. These datasets are typically characterized by similar data distributions and may contain synthetic samples, heuristic labels, or labeling noise. In this study, we systematically evaluate four representative DL models---Devign, ReVeal, LineVul, and VulBERTa---across four representative datasets: Juliet, Devign, BigVul, and ICVul. Each model is trained independently on each dataset, and the graph-based and CodeBERT representations adopted by these models are analyzed using t-SNE and centroid distance to examine vulnerability-related patterns. To assess realistic applicability, we further evaluate trained ReVeal and LineVul models, along with four open-weight LLMs, on VentiVul, our newly constructed temporally separated out-of-distribution (OOD) dataset comprising 200 recent vulnerabilities from Linux and Chromium. Our experiments reveal that current representation methods struggle to distinguish vulnerable from non-vulnerable code and that trained models generalize poorly across datasets with differing distributions and characteristics. When evaluated on VentiVul, performance drops sharply, with most models failing to detect vulnerabilities reliably or distinguish vulnerable functions from their patched counterparts. These results expose a persistent gap between academic benchmarks and real-world deployment, emphasizing the value of our deployment-oriented evaluation framework and the need for more robust code representations, higher-quality datasets, and evaluation methods that account for vulnerability-fixing changes.

cs.CR · cs.LG · cs.SE

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arXiv ↗PDF ↗