10 citations · 14 across the 5 of their papers we have counts for
8 papers
Beyond Pixel Reconstruction: Retrieval-Guided Glyph-Aware Restoration for Low-Resource Manchu Historical Documents
Ting Huang, Dongdong Wang, Mingqiu Liang +1
Historical Manchu documents preserve invaluable linguistic and cultural heritage, yet their digitization is hindered by severe degradations and the scarcity of paired training data…
Think Before You Restore: Risk-Aware Manchu Manuscript Restoration with Stroke-Guided Attention
Mingqiu Liang, Dongdong Wang, Siyang Lu +2
Full-page blind restoration of historical Manchu manuscripts is challenging due to scarce annotations, unknown degradation regions, and fragile connected strokes. Generic restorati…
Can Attack Difficulty Be Characterized Before Optimization? A Study of Pre-optimization Difficulty in Person-Vanishing Attacks
Jingyao Xu, Dongdong Wang, Siyang Lu
Adversarial attacks against object detectors are traditionally studied from an optimization perspective, where attack difficulty is regarded as an outcome observed only after adver…
Too Sure to Be Safe: Model Calibration for Reliable Log Anomaly Detection
Bin Li, Dongdong Wang, Siyang Lu
Online log anomaly detection is critical for maintaining the reliability of large-scale computing systems. Although recent language model-based log anomaly detectors achieve strong…
AWPD: Frequency Shield Network for Agnostic Watermark Presence Detection
Xiang Ao, Yilin Du, Zidan Wang +2
Invisible watermarks, as an essential technology for image copyright protection, have been widely deployed with the rapid development of social media and AIGC. However, existing in…
GLADformer: A Mixed Perspective for Graph-level Anomaly Detection
Fan Xu, Nan Wang, Hao Wu +7
Graph-Level Anomaly Detection (GLAD) aims to distinguish anomalous graphs within a graph dataset. However, current methods are constrained by their receptive fields, struggling to…
Perturbing Attention Gives You More Bang for the Buck: Subtle Imaging Perturbations That Efficiently Fool Customized Diffusion Models
Jingyao Xu, Yuetong Lu, Yandong Li +3
Diffusion models (DMs) embark a new era of generative modeling and offer more opportunities for efficient generating high-quality and realistic data samples. However, their widespr…
SODA: A Semantics-Aware Optimization Framework for Data-Intensive Applications Using Hybrid Program Analysis
Bingbing Rao, Zixia Liu, Hong Zhang +2
In the era of data explosion, a growing number of data-intensive computing frameworks, such as Apache Hadoop and Spark, have been proposed to handle the massive volume of unstructu…