1 citations · 1 across the 7 of their papers we have counts for
7 papers
On the Robustness of Audio Deepfake Detection under Audio Watermarking
Zi Qian Yong, Ajinkya Kulkarni, Julia Lau +4
Recent advances in generative audio models have enabled highly realistic synthetic speech, increasing the importance of reliable audio deepfake detection (ADD) systems. While prior…
AU-Guided Synthetic Video Generation for Micro-Expression Recognition
Pei-Sze Tan, Sailaja Rajanala, Yee-Fan Tan +2
Micro-expression recognition is limited by the small scale, narrow demographic coverage, and restricted emotion labels of existing datasets. We introduce EquiME, a synthetic micro-…
Forgive or forget: Understanding the context of hate in audio retrieval systems
Arghya Pal, Sailaja Rajanala, Raphael C. -W. Phan +1
Handling toxic retrieval in text-to-audio systems is challenging due to contextual dependencies. Existing strategies (e.g., rephrasing, summarization) risk altering intent or omitt…
RECIPER: A Dual-View Retrieval Pipeline for Procedure-Oriented Materials Question Answering
Zhuoyu Wu, Wenhui Ou, Pei-Sze Tan +3
Retrieving procedure-oriented evidence from materials science papers is difficult because key synthesis details are often scattered across long, context-heavy documents and are not…
Causal-Ex: Causal Graph-based Micro and Macro Expression Spotting
Pei-Sze Tan, Sailaja Rajanala, Arghya Pal +2
Detecting concealed emotions within apparently normal expressions is crucial for identifying potential mental health issues and facilitating timely support and intervention. The ta…
Denoising via Repainting: an image denoising method using layer wise medical image repainting
Arghya Pal, Sailaja Rajanala, CheeMing Ting +1
Medical image denoising is essential for improving the reliability of clinical diagnosis and guiding subsequent image-based tasks. In this paper, we propose a multi-scale approach…
Variational Potential Flow: A Novel Probabilistic Framework for Energy-Based Generative Modelling
Junn Yong Loo, Michelle Adeline, Arghya Pal +3
Energy based models (EBMs) are appealing for their generality and simplicity in data likelihood modeling, but have conventionally been difficult to train due to the unstable and ti…