activity
20192023
most citedPalmprint Recognition in Uncontrolled and Uncooperative Environment

143 citations · 342 across the 17 of their papers we have counts for

collaborators

17 papers

cs.CV2023★ 2 cited

Improving Hand Recognition in Uncontrolled and Uncooperative Environments using Multiple Spatial Transformers and Loss Functions

Wojciech Michal Matkowski, Xiaojie Li, Adams Wai Kin Kong

The prevalence of smartphone and consumer camera has led to more evidence in the form of digital images, which are mostly taken in uncontrolled and uncooperative environments. In t…

cs.CV2023★ 2 cited

TF-ICON: Diffusion-Based Training-Free Cross-Domain Image Composition

Shilin Lu, Yanzhu Liu, Adams Wai-Kin Kong

Text-driven diffusion models have exhibited impressive generative capabilities, enabling various image editing tasks. In this paper, we propose TF-ICON, a novel Training-Free Image…

cs.CV2023

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…

cs.LG2023

A Practical Upper Bound for the Worst-Case Attribution Deviations

Fan Wang, Adams Wai-Kin Kong

Model attribution is a critical component of deep neural networks (DNNs) for its interpretability to complex models. Recent studies bring up attention to the security of attributio…

cs.CV2023★ 1 cited

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…

cs.SD2023

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…