1 citations · 2 across the 5 of their papers we have counts for
5 papers
Altogether: Image Captioning via Re-aligning Alt-text
Hu Xu, Po-Yao Huang, Xiaoqing Ellen Tan +10
This paper focuses on creating synthetic data to improve the quality of image captions. Existing works typically have two shortcomings. First, they caption images from scratch, ign…
Text Quality-Based Pruning for Efficient Training of Language Models
Vasu Sharma, Karthik Padthe, Newsha Ardalani +8
In recent times training Language Models (LMs) have relied on computationally heavy training over massive datasets which makes this training process extremely laborious. In this pa…
MoDE: CLIP Data Experts via Clustering
Jiawei Ma, Po-Yao Huang, Saining Xie +5
The success of contrastive language-image pretraining (CLIP) relies on the supervision from the pairing between images and captions, which tends to be noisy in web-crawled data. We…
FLAP: Fast Language-Audio Pre-training
Ching-Feng Yeh, Po-Yao Huang, Vasu Sharma +2
We propose Fast Language-Audio Pre-training (FLAP), a self-supervised approach that efficiently and effectively learns aligned audio and language representations through masking, c…
AV-SUPERB: A Multi-Task Evaluation Benchmark for Audio-Visual Representation Models
Yuan Tseng, Layne Berry, Yi-Ting Chen +16
Audio-visual representation learning aims to develop systems with human-like perception by utilizing correlation between auditory and visual information. However, current models of…