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Kha Gia Quach

7 accepted papers

2023

Type-to-Track: Retrieve Any Object via Prompt-based Tracking

NeurIPS 2023poster

One of the recent trends in vision problems is to use natural language captions to describe the objects of interest. This approach can overcome some limitations of traditional methods that rely on bounding boxes or category annotations. This paper introduces a novel paradigm for Multiple Object Trac…

Cited by 24SourcePDFScholar
2021

DyGLIP: A Dynamic Graph Model With Link Prediction for Accurate Multi-Camera Multiple Object Tracking

CVPR 2021poster

Multi-Camera Multiple Object Tracking (MC-MOT) is a significant computer vision problem due to its emerging applicability in several real-world applications. Despite a large number of existing works, solving the data association problem in any MC-MOT pipeline is arguably one of the most challenging…

Cited by 71PDFcodeScholar
2020

Vec2Face: Unveil Human Faces From Their Blackbox Features in Face Recognition

CVPR 2020oral

Unveiling face images of a subject given his/her high-level representations extracted from a blackbox Face Recognition engine is extremely challenging. It is because the limitations of accessible information from that engine including its structure and uninterpretable extracted features. This paper…

Cited by 62PDFScholar
2019

Automatic Face Aging in Videos via Deep Reinforcement Learning

CVPR 2019poster

This paper presents a novel approach for synthesizing automatically age-progressed facial images in video sequences using Deep Reinforcement Learning. The proposed method models facial structures and the longitudinal face-aging process of given subjects coherently across video frames. The approach i…

Cited by 46PDFScholar
2017

Temporal Non-Volume Preserving Approach to Facial Age-Progression and Age-Invariant Face Recognition

ICCV 2017oral

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. In order to efficiently address the problem, this work first decomposes the aging process into multiple short-term stages. Then, a novel gen…

Cited by 97PDFScholar
2016

Longitudinal Face Modeling via Temporal Deep Restricted Boltzmann Machines

CVPR 2016poster

Modeling the face aging process is a challenging task due to large and non-linear variations present in different stages of face development. This paper presents a deep model approach for face age progression that can efficiently capture the non-linear aging process and automatically synthesize a se…

Cited by 71PDFScholar
2015

Beyond Principal Components: Deep Boltzmann Machines for Face Modeling

CVPR 2015poster

The "interpretation through synthesis", i.e. Active Appearance Models (AAMs) method, has received considerable attention over the past decades. It aims at "explaining" face images by synthesizing them via a parameterized model of appearance. It is quite challenging due to appearance variations of hu…

Cited by 60SourcePDFScholar