25 citations · 40 across the 24 of their papers we have counts for
8 papers · 1 filter
VideoWebArena: Evaluating Long Context Multimodal Agents with Video Understanding Web Tasks
Lawrence Jang, Yinheng Li, Dan Zhao +5
Videos are often used to learn or extract the necessary information to complete tasks in ways different than what text and static imagery alone can provide. However, many existing…
Zero-Shot Text-to-Speech from Continuous Text Streams
Trung Dang, David Aponte, Dung Tran +2
Existing zero-shot text-to-speech (TTS) systems are typically designed to process complete sentences and are constrained by the maximum duration for which they have been trained. H…
Windows Agent Arena: Evaluating Multi-Modal OS Agents at Scale
Rogerio Bonatti, Dan Zhao, Francesco Bonacci +9
Large language models (LLMs) show remarkable potential to act as computer agents, enhancing human productivity and software accessibility in multi-modal tasks that require planning…
Data Generation Using Large Language Models for Text Classification: An Empirical Case Study
Yinheng Li, Rogerio Bonatti, Sara Abdali +2
Using Large Language Models (LLMs) to generate synthetic data for model training has become increasingly popular in recent years. While LLMs are capable of producing realistic trai…
LiveSpeech: Low-Latency Zero-shot Text-to-Speech via Autoregressive Modeling of Audio Discrete Codes
Trung Dang, David Aponte, Dung Tran +1
Prior works have demonstrated zero-shot text-to-speech by using a generative language model on audio tokens obtained via a neural audio codec. It is still challenging, however, to…
Weakly-supervised Audio Separation via Bi-modal Semantic Similarity
Tanvir Mahmud, Saeed Amizadeh, Kazuhito Koishida +1
Conditional sound separation in multi-source audio mixtures without having access to single source sound data during training is a long standing challenge. Existing mix-and-separat…
uaMix-MAE: Efficient Tuning of Pretrained Audio Transformers with Unsupervised Audio Mixtures
Afrina Tabassum, Dung Tran, Trung Dang +2
Masked Autoencoders (MAEs) learn rich low-level representations from unlabeled data but require substantial labeled data to effectively adapt to downstream tasks. Conversely, Insta…
Learned Image Compression with Text Quality Enhancement
Chih-Yu Lai, Dung Tran, Kazuhito Koishida
Learned image compression has gained widespread popularity for their efficiency in achieving ultra-low bit-rates. Yet, images containing substantial textual content, particularly s…