3 citations · 8 across the 9 of their papers we have counts for
10 papers
SVC 2025: the First Multimodal Deception Detection Challenge
Xun Lin, Xiaobao Guo, Taorui Wang +5
Deception detection is a critical task in real-world applications such as security screening, fraud prevention, and credibility assessment. While deep learning methods have shown p…
SparseMamba-PCL: Scribble-Supervised Medical Image Segmentation via SAM-Guided Progressive Collaborative Learning
Luyi Qiu, Tristan Till, Xiaobao Guo +1
Scribble annotations significantly reduce the cost and labor required for dense labeling in large medical datasets with complex anatomical structures. However, current scribble-sup…
MACS: Multi-source Audio-to-image Generation with Contextual Significance and Semantic Alignment
Hao Zhou, Xiaobao Guo, Yuzhe Zhu +1
Propelled by the breakthrough in deep generative models, audio-to-image generation has emerged as a pivotal cross-modal task that converts complex auditory signals into rich visual…
Benchmarking Cross-Domain Audio-Visual Deception Detection
Xiaobao Guo, Zitong Yu, Nithish Muthuchamy Selvaraj +3
Automated deception detection is crucial for assisting humans in accurately assessing truthfulness and identifying deceptive behavior. Conventional contact-based techniques, like p…
Improving Concept Alignment in Vision-Language Concept Bottleneck Models
Nithish Muthuchamy Selvaraj, Xiaobao Guo, Adams Wai-Kin Kong +1
Concept Bottleneck Models (CBM) map images to human-interpretable concepts before making class predictions. Recent approaches automate CBM construction by prompting Large Language…
MIMIC: Mask Image Pre-training with Mix Contrastive Fine-tuning for Facial Expression Recognition
Fan Zhang, Xiaobao Guo, Xiaojiang Peng +1
Cutting-edge research in facial expression recognition (FER) currently favors the utilization of convolutional neural networks (CNNs) backbone which is supervisedly pre-trained on…
Audio-Visual Deception Detection: DOLOS Dataset and Parameter-Efficient Crossmodal Learning
Xiaobao Guo, Nithish Muthuchamy Selvaraj, Zitong Yu +3
Deception detection in conversations is a challenging yet important task, having pivotal applications in many fields such as credibility assessment in business, multimedia anti-fra…
Retrieving Multimodal Information for Augmented Generation: A Survey
Ruochen Zhao, Hailin Chen, Weishi Wang +8
As Large Language Models (LLMs) become popular, there emerged an important trend of using multimodality to augment the LLMs' generation ability, which enables LLMs to better intera…
Flexible-modal Deception Detection with Audio-Visual Adapter
Zhaoxu Li, Zitong Yu, Nithish Muthuchamy Selvaraj +4
Detecting deception by human behaviors is vital in many fields such as custom security and multimedia anti-fraud. Recently, audio-visual deception detection attracts more attention…
Adapter Incremental Continual Learning of Efficient Audio Spectrogram Transformers
Nithish Muthuchamy Selvaraj, Xiaobao Guo, Adams Kong +2
Continual learning involves training neural networks incrementally for new tasks while retaining the knowledge of previous tasks. However, efficiently fine-tuning the model for seq…