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20172024
most citedMicron-BERT: BERT-based Facial Micro-Expression Recognition

6 citations · 10 across the 9 of their papers we have counts for

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8 papers · 1 filter

cs.CV2024

ED-SAM: An Efficient Diffusion Sampling Approach to Domain Generalization in Vision-Language Foundation Models

Thanh-Dat Truong, Xin Li, Bhiksha Raj +2

The Vision-Language Foundation Model has recently shown outstanding performance in various perception learning tasks. The outstanding performance of the vision-language model mainl…

cs.CV20232 cited

The Algonauts Project 2023 Challenge: UARK-UAlbany Team Solution

Xuan-Bac Nguyen, Xudong Liu, Xin Li +1

This work presents our solutions to the Algonauts Project 2023 Challenge. The primary objective of the challenge revolves around employing computational models to anticipate brain…

cs.CV20231 cited

CoMaL: Conditional Maximum Likelihood Approach to Self-supervised Domain Adaptation in Long-tail Semantic Segmentation

Thanh-Dat Truong, Chi Nhan Duong, Pierce Helton +3

The research in self-supervised domain adaptation in semantic segmentation has recently received considerable attention. Although GAN-based methods have become one of the most popu…

cs.CV20231 cited

CROVIA: Seeing Drone Scenes from Car Perspective via Cross-View Adaptation

Thanh-Dat Truong, Chi Nhan Duong, Ashley Dowling +3

Understanding semantic scene segmentation of urban scenes captured from the Unmanned Aerial Vehicles (UAV) perspective plays a vital role in building a perception model for UAV. Wi…

cs.CV20236 cited

Micron-BERT: BERT-based Facial Micro-Expression Recognition

Xuan-Bac Nguyen, Chi Nhan Duong, Xin Li +3

Micro-expression recognition is one of the most challenging topics in affective computing. It aims to recognize tiny facial movements difficult for humans to perceive in a brief pe…

cs.CV2023

FREDOM: Fairness Domain Adaptation Approach to Semantic Scene Understanding

Thanh-Dat Truong, Ngan Le, Bhiksha Raj +2

Although Domain Adaptation in Semantic Scene Segmentation has shown impressive improvement in recent years, the fairness concerns in the domain adaptation have yet to be well defin…