10 papers
DuoAD: Leveraging [CLS] Dual Characteristics for Training-Free Few-Shot Anomaly Detection
Jyun-Ze Tang, Po-Han Huang, Ming-Ching Chang +2
Vision foundation models have enabled strong training-free anomaly detection (AD). However, most existing approaches rely primarily on independent local patch features, leaving the…
SeamlessEdit: Background Noise Aware Zero-Shot Speech Editing with in-Context Enhancement
Kuan-Yu Chen, Jeng-Lin Li, De-Yan Lu +1
With the fast development of zero-shot text-to-speech technologies, it is possible to generate high-quality speech signals that are indistinguishable from the real ones. Speech edi…
The Binding Effect: Analyzing How Multi-Dimensional Cues Form Gender Bias in Instruction TTS
Kuan-Yu Chen, Yi-Cheng Lin, Po-Chung Hsieh +5
Current bias evaluations in Instruction Text-to-Speech (ITTS) often rely on univariate testing, overlooking the compositional structure of social cues. In this work, we investigate…
Bloodroot: When Watermarking Turns Poisonous For Stealthy Backdoor
Kuan-Yu Chen, Yi-Cheng Lin, Jeng-Lin Li +1
Backdoor data poisoning is a crucial technique for ownership protection and defending against malicious attacks. Embedding hidden triggers in training data can manipulate model out…
PatchEAD: Unifying Industrial Visual Prompting Frameworks for Patch-Exclusive Anomaly Detection
Po-Han Huang, Jeng-Lin Li, Po-Hsuan Huang +2
Industrial anomaly detection is increasingly relying on foundation models, aiming for strong out-of-distribution generalization and rapid adaptation in real-world deployments. Nota…
Dual-Process Scaffold Reasoning for Enhancing LLM Code Debugging
Po-Chung Hsieh, Chin-Po Chen, Jeng-Lin Li +1
Recent LLMs have demonstrated sophisticated problem-solving capabilities on various benchmarks through advanced reasoning algorithms. However, the key research question of identify…