no code implementations • 7 Oct 2022 • Ananda Padhmanabhan Suresh, Sanjana Jain, Pavit Noinongyao, Ankush Ganguly, Ukrit Watchareeruetai, Aubin Samacoits
In recent years, language-driven artistic style transfer has emerged as a new type of style transfer technique, eliminating the need for a reference style image by using natural language descriptions of the style.
no code implementations • 22 Sep 2022 • Ankush Ganguly, Sanjana Jain, Ukrit Watchareeruetai
The core principle of Variational Inference (VI) is to convert the statistical inference problem of computing complex posterior probability densities into a tractable optimization problem.
no code implementations • 30 Dec 2021 • Benjaphan Sommana, Ukrit Watchareeruetai, Ankush Ganguly, Samuel W. F. Earp, Taya Kitiyakara, Suparee Boonmanunt, Ratchainant Thammasudjarit
In this paper, we present a two-step face mask detection approach consisting of two separate modules: 1) face detection and alignment and 2) face mask classification.
no code implementations • 21 Sep 2021 • Ukrit Watchareeruetai, Benjaphan Sommana, Sanjana Jain, Pavit Noinongyao, Ankush Ganguly, Aubin Samacoits, Samuel W. F. Earp, Nakarin Sritrakool
The proposed framework is a direct coordinate regression approach leveraging a Transformer network to better utilize the spatial information in the feature map.
Ranked #13 on Face Alignment on WFLW
no code implementations • 30 Aug 2021 • Ankush Ganguly, Samuel W. F. Earp
In this paper, we introduce the concept of Variational Inference (VI), a popular method in machine learning that uses optimization techniques to estimate complex probability densities.
no code implementations • 2 Dec 2019 • Samuel W. F. Earp, Pavit Noinongyao, Justin A. Cairns, Ankush Ganguly
Accurate face detection and facial landmark localization are crucial to any face recognition system.