no code implementations • 14 Apr 2023 • Thanh-Dat Truong, Chi Nhan Duong, Ashley Dowling, Son Lam Phung, Jackson Cothren, Khoa Luu
First, a novel geometry-based constraint to cross-view adaptation is introduced based on the geometry correlation between views.
no code implementations • 14 Apr 2023 • Thanh-Dat Truong, Chi Nhan Duong, Pierce Helton, Ashley Dowling, Xin Li, Khoa Luu
They are insufficient to model both global and local structures of a given image, especially in small regions of tail classes.
no code implementations • 14 Apr 2023 • Xuan-Bac Nguyen, Chi Nhan Duong, Marios Savvides, Kaushik Roy, Hugh Churchill, Khoa Luu
Promoting fairness for deep clustering models in unsupervised clustering settings to reduce demographic bias is a challenging goal.
1 code implementation • CVPR 2023 • Xuan-Bac Nguyen, Chi Nhan Duong, Xin Li, Susan Gauch, Han-Seok Seo, Khoa Luu
By incorporating these components into an end-to-end deep network, the proposed $\mu$-BERT significantly outperforms all previous work in various micro-expression tasks.
Ranked #1 on Micro Expression Recognition on SMIC
Micro Expression Recognition Micro-Expression Recognition +1
no code implementations • 17 Nov 2022 • Pha Nguyen, Kha Gia Quach, Chi Nhan Duong, Son Lam Phung, Ngan Le, Khoa Luu
The development of autonomous vehicles generates a tremendous demand for a low-cost solution with a complete set of camera sensors capturing the environment around the car.
no code implementations • 11 Sep 2022 • Thanh-Dat Truong, Chi Nhan Duong, Ngan Le, Marios Savvides, Khoa Luu
We therefore introduce a new method named Attention-based Bijective Generative Adversarial Networks in a Distillation framework (DAB-GAN) to synthesize faces of a subject given his/her extracted face recognition features.
no code implementations • 10 Jul 2022 • Kha Gia Quach, Huu Le, Pha Nguyen, Chi Nhan Duong, Tien Dai Bui, Khoa Luu
This paper aims to tackle Multiple Object Tracking (MOT), an important problem in computer vision but remains challenging due to many practical issues, especially occlusions.
no code implementations • 19 Apr 2022 • Pha Nguyen, Kha Gia Quach, Chi Nhan Duong, Ngan Le, Xuan-Bac Nguyen, Khoa Luu
The experimental results on the nuScenes dataset demonstrate the benefits of the proposed method to produce SOTA performance on the existing vision-based tracking dataset.
1 code implementation • CVPR 2022 • Thanh-Dat Truong, Quoc-Huy Bui, Chi Nhan Duong, Han-Seok Seo, Son Lam Phung, Xin Li, Khoa Luu
Various 3D-CNN based methods have been presented to tackle both the spatial and temporal dimensions in the task of video action recognition with competitive results.
Ranked #1 on Action Recognition on Jester (Gesture Recognition)
1 code implementation • ICCV 2021 • Thanh-Dat Truong, Chi Nhan Duong, Ngan Le, Son Lam Phung, Chase Rainwater, Khoa Luu
Semantic segmentation aims to predict pixel-level labels.
1 code implementation • ICCV 2021 • Thanh-Dat Truong, Chi Nhan Duong, The De Vu, Hoang Anh Pham, Bhiksha Raj, Ngan Le, Khoa Luu
Therefore, this work introduces a new Audio-Visual Transformer approach to the problem of localization and highlighting the main speaker in both audio and visual channels of a multi-speaker conversation video in the wild.
1 code implementation • CVPR 2021 • Xuan-Bac Nguyen, Duc Toan Bui, Chi Nhan Duong, Tien D. Bui, Khoa Luu
This work therefore presents the Clusformer, a simple but new perspective of Transformer based approach, to automatic visual clustering via its unsupervised attention mechanism.
1 code implementation • CVPR 2021 • Kha Gia Quach, Pha Nguyen, Huu Le, Thanh-Dat Truong, Chi Nhan Duong, Minh-Triet Tran, Khoa Luu
Multi-Camera Multiple Object Tracking (MC-MOT) is a significant computer vision problem due to its emerging applicability in several real-world applications.
no code implementations • 9 Apr 2020 • Thanh-Dat Truong, Chi Nhan Duong, Kha Gia Quach, Ngan Le, Tien D. Bui, Khoa Luu
This work presents a novel Lightweight Attentive Angular Distillation (LIAAD) approach to Large-scale Lightweight AiFR that overcomes these limitations.
no code implementations • CVPR 2020 • Chi Nhan Duong, Thanh-Dat Truong, Kha Gia Quach, Hung Bui, Kaushik Roy, Khoa Luu
Unveiling face images of a subject given his/her high-level representations extracted from a blackbox Face Recognition engine is extremely challenging.
no code implementations • 28 May 2019 • Thanh-Dat Truong, Chi Nhan Duong, Khoa Luu, Minh-Triet Tran, Ngan Le
However, it has been largely overlooked in the problem of recognition in new unseen domains.
no code implementations • 28 May 2019 • Thanh-Dat Truong, Khoa Luu, Chi Nhan Duong, Ngan Le, Minh-Triet Tran
This paper presents a novel deep learning based approach to tackle the problem of across unseen modalities.
2 code implementations • 25 May 2019 • Chi Nhan Duong, Khoa Luu, Kha Gia Quach, Ngan Le
In addition, this work introduces a novel Angular Distillation Loss for distilling the feature direction and the sample distributions of the teacher's hypersphere to its student.
no code implementations • 24 May 2019 • Thanh-Dat Truong, Khoa Luu, Chi Nhan Duong, Ngan Le, Minh-Triet Tran
The experiments on CIFAR-10, ImageNet and Celeb-HQ datasets, have shown that our invertible $n \times n$ convolution helps to improve the performance of generative models significantly.
no code implementations • 9 Dec 2018 • Thanh-Dat Truong, Chi Nhan Duong, Khoa Luu, Minh-Triet Tran, Minh Do
However, it has been largely overlooked in the problem of recognition in new unseen domains.
no code implementations • 28 Nov 2018 • Kha Gia Quach, Ngan Le, Chi Nhan Duong, Ibsa Jalata, Kaushik Roy, Khoa Luu
To demonstrate the robustness and effectiveness of each component in the proposed approach, three experiments were conducted: (i) evaluation on AffectNet database to benchmark the proposed EmoNet for recognizing facial expression; (ii) evaluation on EmotiW2018 to benchmark the proposed deep feature level fusion mechanism NVPF; and, (iii) examine the proposed TNVPF on an innovative Group-level Emotion on Crowd Videos (GECV) dataset composed of 627 videos collected from publicly available sources.
no code implementations • CVPR 2019 • Chi Nhan Duong, Khoa Luu, Kha Gia Quach, Nghia Nguyen, Eric Patterson, Tien D. Bui, Ngan Le
This paper presents a novel approach to synthesize automatically age-progressed facial images in video sequences using Deep Reinforcement Learning.
no code implementations • 27 Nov 2018 • Chi Nhan Duong, Kha Gia Quach, Ibsa Jalata, Ngan Le, Khoa Luu
Deep neural networks have been widely used in numerous computer vision applications, particularly in face recognition.
no code implementations • 23 Feb 2018 • Chi Nhan Duong, Khoa Luu, Kha Gia Quach, Tien D. Bui
Face Aging has raised considerable attentions and interest from the computer vision community in recent years.
no code implementations • 28 Nov 2017 • Chi Nhan Duong, Kha Gia Quach, Khoa Luu, T. Hoang Ngan Le, Marios Savvides, Tien D. Bui
The proposed model is experimented in both tasks of face aging synthesis and cross-age face verification.
no code implementations • 12 Apr 2017 • T. Hoang Ngan Le, Chi Nhan Duong, Ligong Han, Khoa Luu, Marios Savvides, Dipan Pal
Designed as extremely deep architectures, deep residual networks which provide a rich visual representation and offer robust convergence behaviors have recently achieved exceptional performance in numerous computer vision problems.
no code implementations • ICCV 2017 • Chi Nhan Duong, Kha Gia Quach, Khoa Luu, T. Hoang Ngan Le, Marios Savvides
Modeling the long-term facial aging process is extremely challenging due to the presence of large and non-linear variations during the face development stages.
no code implementations • 23 Jul 2016 • Chi Nhan Duong, Khoa Luu, Kha Gia Quach, Tien D. Bui
This paper presents a novel Deep Appearance Models (DAMs) approach, an efficient replacement for AAMs, to accurately capture both shape and texture of face images under large variations.
no code implementations • 3 Jul 2016 • Kha Gia Quach, Chi Nhan Duong, Khoa Luu, Tien D. Bui
In this approach, two crucial components of face images, i. e. shape and texture, are represented by Deep Boltzmann Machines and Robust Deep Boltzmann Machines (RDBM), respectively.
no code implementations • CVPR 2016 • Chi Nhan Duong, Khoa Luu, Kha Gia Quach, Tien D. Bui
The Temporal Deep Restricted Boltzmann Machines based age progression model together with the prototype faces are then constructed to learn the aging transformation between faces in the sequence.
no code implementations • CVPR 2015 • Chi Nhan Duong, Khoa Luu, Kha Gia Quach, Tien D. Bui
The "interpretation through synthesis", i. e.