Search Results for author:
Found 10 papers, 4 papers with code
C-TPT: Calibrated Test-Time Prompt Tuning for Vision-Language Models via Text Feature Dispersion
Through a series of observations, we find that the prompt choice significantly affects the calibration in CLIP, where the prompts leading to higher text feature dispersion result in better-calibrated predictions.
AdaMER-CTC: Connectionist Temporal Classification with Adaptive Maximum Entropy Regularization for Automatic Speech Recognition
In Automatic Speech Recognition (ASR) systems, a recurring obstacle is the generation of narrowly focused output distributions.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
HEAR: Hearing Enhanced Audio Response for Video-grounded Dialogue
Video-grounded Dialogue (VGD) aims to answer questions regarding a given multi-modal input comprising video, audio, and dialogue history.
SimPSI: A Simple Strategy to Preserve Spectral Information in Time Series Data Augmentation
Our experimental results support that SimPSI considerably enhances the performance of time series data augmentations by preserving core spectral information.
Mitigating the Exposure Bias in Sentence-Level Grapheme-to-Phoneme (G2P) Transduction
Text-to-Text Transfer Transformer (T5) has recently been considered for the Grapheme-to-Phoneme (G2P) transduction.
INTapt: Information-Theoretic Adversarial Prompt Tuning for Enhanced Non-Native Speech Recognition
INTapt is trained simultaneously in the following two manners: (1) adversarial training to reduce accent feature dependence between the original input and the prompt-concatenated input and (2) training to minimize CTC loss for improving ASR performance to a prompt-concatenated input.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
ESD: Expected Squared Difference as a Tuning-Free Trainable Calibration Measure
Studies have shown that modern neural networks tend to be poorly calibrated due to over-confident predictions.
SMSMix: Sense-Maintained Sentence Mixup for Word Sense Disambiguation
To the best of our knowledge, this is the first attempt to apply mixup in NLP while preserving the meaning of a specific word.
Information-Theoretic Text Hallucination Reduction for Video-grounded Dialogue
Despite the recent success of multi-modal reasoning to generate answer sentences, existing dialogue systems still suffer from a text hallucination problem, which denotes indiscriminate text-copying from input texts without an understanding of the question.
Selective Query-guided Debiasing for Video Corpus Moment Retrieval
Video moment retrieval (VMR) aims to localize target moments in untrimmed videos pertinent to a given textual query.
