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20172025
most citedA Bayesian Data Augmentation Approach for Learning Deep Models

118 citations · 123 across the 4 of their papers we have counts for

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6 papers · 1 filter

cs.CV2025

Marginalized Generalized IoU (MGIoU): A Unified Objective Function for Optimizing Any Convex Parametric Shapes

Duy-Tho Le, Trung Pham, Jianfei Cai +1

Optimizing the similarity between parametric shapes is crucial for numerous computer vision tasks, where Intersection over Union (IoU) stands as the canonical measure. However, exi…

cs.CV2023

DPPD: Deformable Polar Polygon Object Detection

Yang Zheng, Oles Andrienko, Yonglei Zhao +2

Regular object detection methods output rectangle bounding boxes, which are unable to accurately describe the actual object shapes. Instance segmentation methods output pixel-level…

cs.CV2018

Bayesian Semantic Instance Segmentation in Open Set World

Trung Pham, Vijay Kumar B G, Thanh-Toan Do +2

This paper addresses the semantic instance segmentation task in the open-set conditions, where input images can contain known and unknown object classes. The training process of ex…

cs.CV2018

Deep-6DPose: Recovering 6D Object Pose from a Single RGB Image

Thanh-Toan Do, Ming Cai, Trung Pham +1

Detecting objects and their 6D poses from only RGB images is an important task for many robotic applications. While deep learning methods have made significant progress in visual o…

cs.CV2018

Binary Constrained Deep Hashing Network for Image Retrieval without Manual Annotation

Thanh-Toan Do, Tuan Hoang, Dang-Khoa Le Tan +4

Learning compact binary codes for image retrieval task using deep neural networks has attracted increasing attention recently. However, training deep hashing networks for the task…

cs.CV2017118 cited

A Bayesian Data Augmentation Approach for Learning Deep Models

Toan Tran, Trung Pham, Gustavo Carneiro +2

Data augmentation is an essential part of the training process applied to deep learning models. The motivation is that a robust training process for deep learning models depends on…