Search Results for author: Trung X. Pham

Found 7 papers, 3 papers with code

Dual Temperature Helps Contrastive Learning Without Many Negative Samples: Towards Understanding and Simplifying MoCo

2 code implementations CVPR 2022 Chaoning Zhang, Kang Zhang, Trung X. Pham, Axi Niu, Zhinan Qiao, Chang D. Yoo, In So Kweon

Contrastive learning (CL) is widely known to require many negative samples, 65536 in MoCo for instance, for which the performance of a dictionary-free framework is often inferior because the negative sample size (NSS) is limited by its mini-batch size (MBS).

Contrastive Learning

Self-supervised Learning with Local Attention-Aware Feature

no code implementations1 Aug 2021 Trung X. Pham, Rusty John Lloyd Mina, Dias Issa, Chang D. Yoo

In this work, we propose a novel methodology for self-supervised learning for generating global and local attention-aware visual features.

Self-Supervised Learning

Cascade RPN: Delving into High-Quality Region Proposal Network with Adaptive Convolution

2 code implementations NeurIPS 2019 Thang Vu, Hyunjun Jang, Trung X. Pham, Chang D. Yoo

This paper considers an architecture referred to as Cascade Region Proposal Network (Cascade RPN) for improving the region-proposal quality and detection performance by \textit{systematically} addressing the limitation of the conventional RPN that \textit{heuristically defines} the anchors and \textit{aligns} the features to the anchors.

Ranked #158 on Object Detection on COCO test-dev (using extra training data)

Object Detection Region Proposal

Fast and Efficient Image Quality Enhancement via Desubpixel Convolutional Neural Networks

1 code implementation ECCV2018 2018 Thang Vu, Cao V. Nguyen, Trung X. Pham, Tung M. Luu, Chang D. Yoo

This paper considers a convolutional neural network for image quality enhancement referred to as the fast and efficient quality enhancement (FEQE) that can be trained for either image super-resolution or image enhancement to provide accurate yet visually pleasing images on mobile devices by addressing the following three main issues.

Image Enhancement Image Super-Resolution

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