Publications | Dat Viet Thanh Nguyen

SEMACS: Semantic–Acoustic Speech Coding for Ultra-Low Token Rates

Thu, 01 Jan 2026 00:00:00 +0000

This manuscript is currently under review. A link to the preprint, code, and full details will be added here once it is publicly available.

Driver Attention Tracking and Analysis

Wed, 10 Apr 2024 00:00:00 +0000

Why this problem

Knowing what a driver is looking at — not just where their head is pointing — is the difference between a monitoring system that can warn about a missed traffic sign and one that cannot. The usual answer is eye-tracking glasses, which are expensive, intrusive, and impractical for mass deployment. They also only tell you what the driver did look at, never what they missed in the scene.

What we did

We built a dashboard-mounted system that needs only two ordinary cameras: one on the driver’s face, one on the road. The core is a convolutional network that reads both images at once and predicts the driver’s point-of-gaze directly in the scene frame.

The hard part is that the geometry between driver and cameras drifts constantly — even within a single drive. Rather than asking the driver to recalibrate, the network includes a camera calibration module that learns an embedding of the driver–camera spatial configuration and is trained end to end with the rest of the model. This module measurably improves gaze accuracy.

We also released a large-scale in-situ dataset: real urban driving sessions with synchronised face, scene, and gaze video, and over one hundred thousand image pairs annotated with points of gaze — addressing a gap no existing gaze dataset filled.

Result

Mean prediction error of 29.69 pixels against a 1280×720 scene camera, outperforming the baseline methods we compared against.

QC-StyleGAN: Quality Controllable Image Generation and Manipulation

Mon, 28 Nov 2022 00:00:00 +0000

Why this problem

StyleGAN and its successors are trained on curated, high-quality datasets. That is fine for synthesis, but it breaks the moment you want to edit a real photo: GAN inversion pushes a blurry, noisy, or JPEG-compressed input onto a high-quality manifold, so the model silently “restores” the image while you were only trying to change the hairstyle. You lose the ability to make an edit that preserves the original look.

What we did

We added an explicit quality control code to the generator, so image content and image degradation become separately controllable. The same network can:

synthesise images at any point along a continuous quality axis;
invert and edit a low-quality image without changing its quality;
restore a sharp image by simply moving the quality code — giving blind restoration across noise, blur, compression artifacts, and their mixtures, without training a restoration model;
synthesise, transfer, and interpolate degradations between images.

Published at NeurIPS 2022; the work also received a NeurIPS Scholar Award. Code is available in the official repository.