2 citations · 6 across the 5 of their papers we have counts for
5 papers
VORD: Visual Ordinal Calibration for Mitigating Object Hallucinations in Large Vision-Language Models
Dexter Neo, Tsuhan Chen
Large Vision-Language Models (LVLMs) have made remarkable developments along with the recent surge of large language models. Despite their advancements, LVLMs have a tendency to ge…
FER-C: Benchmarking Out-of-Distribution Soft Calibration for Facial Expression Recognition
Dexter Neo, Tsuhan Chen
We present a soft benchmark for calibrating facial expression recognition (FER). While prior works have focused on identifying affective states, we find that FER models are uncalib…
DSAC-C: Constrained Maximum Entropy for Robust Discrete Soft-Actor Critic
Dexter Neo, Tsuhan Chen
We present a novel extension to the family of Soft Actor-Critic (SAC) algorithms. We argue that based on the Maximum Entropy Principle, discrete SAC can be further improved via add…
MaxEnt Loss: Constrained Maximum Entropy for Calibration under Out-of-Distribution Shift
Dexter Neo, Stefan Winkler, Tsuhan Chen
We present a new loss function that addresses the out-of-distribution (OOD) calibration problem. While many objective functions have been proposed to effectively calibrate models i…
Morphset:Augmenting categorical emotion datasets with dimensional affect labels using face morphing
Vassilios Vonikakis, Dexter Neo, Stefan Winkler
Emotion recognition and understanding is a vital component in human-machine interaction. Dimensional models of affect such as those using valence and arousal have advantages over t…