19 citations · 39 across the 13 of their papers we have counts for
18 papers
MetaAug: Meta-Data Augmentation for Post-Training Quantization
Cuong Pham, Hoang Anh Dung, Cuong C. Nguyen +4
Post-Training Quantization (PTQ) has received significant attention because it requires only a small set of calibration data to quantize a full-precision model, which is more pract…
Bayesian Detector Combination for Object Detection with Crowdsourced Annotations
Zhi Qin Tan, Olga Isupova, Gustavo Carneiro +2
Acquiring fine-grained object detection annotations in unconstrained images is time-consuming, expensive, and prone to noise, especially in crowdsourcing scenarios. Most prior obje…
Learning to Complement and to Defer to Multiple Users
Zheng Zhang, Wenjie Ai, Kevin Wells +3
With the development of Human-AI Collaboration in Classification (HAI-CC), integrating users and AI predictions becomes challenging due to the complex decision-making process. This…
Model and Feature Diversity for Bayesian Neural Networks in Mutual Learning
Cuong Pham, Cuong C. Nguyen, Trung Le +3
Bayesian Neural Networks (BNNs) offer probability distributions for model parameters, enabling uncertainty quantification in predictions. However, they often underperform compared…
CPM: Class-conditional Prompting Machine for Audio-visual Segmentation
Yuanhong Chen, Chong Wang, Yuyuan Liu +2
Audio-visual segmentation (AVS) is an emerging task that aims to accurately segment sounding objects based on audio-visual cues. The success of AVS learning systems depends on the…
Frequency Attention for Knowledge Distillation
Cuong Pham, Van-Anh Nguyen, Trung Le +3
Knowledge distillation is an attractive approach for learning compact deep neural networks, which learns a lightweight student model by distilling knowledge from a complex teacher…