Viet-Quoc Pham — accepted AI-conference papers

2023

Focusing on Targets for Improving Weakly Supervised Visual Grounding

ICASSP 2023accepted

Weakly supervised visual grounding aims to predict the region in an image that corresponds to a specific linguistic query, where the mapping between the target object and query is unknown in the training stage. The state-of-the-art method uses a vision language pre-training model to acquire heatmaps…

Cited by 0 Source ↗Scholar ↗

2015

COUNT Forest: CO-Voting Uncertain Number of Targets Using Random Forest for Crowd Density Estimation

Viet-Quoc Pham, Tatsuo Kozakaya, Osamu Yamaguchi, Ryuzo Okada

ICCV 2015poster

This paper presents a patch-based approach for crowd density estimation in public scenes. We formulate the problem of estimating density in a structured learning framework applied to random decision forests. Our approach learns the mapping between patch features and relative locations of all objects…

Cited by 440 PDF ↗Scholar ↗