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of 951
pro vyhledávání: '"Tran, Son"'
Autor:
Tran, Son Quoc, Kretchmar, Matt
This paper proposes a novel training method to improve the robustness of Extractive Question Answering (EQA) models. Previous research has shown that existing models, when trained on EQA datasets that include unanswerable questions, demonstrate a sig
Externí odkaz:
http://arxiv.org/abs/2409.19766
Autor:
Chen, Changyou, Ding, Han, Sisman, Bunyamin, Xu, Yi, Xie, Ouye, Yao, Benjamin Z., Tran, Son Dinh, Zeng, Belinda
Diffusion-based generative modeling has been achieving state-of-the-art results on various generation tasks. Most diffusion models, however, are limited to a single-generation modeling. Can we generalize diffusion models with the ability of multi-mod
Externí odkaz:
http://arxiv.org/abs/2407.17571
Autor:
Swetha, Sirnam, Yang, Jinyu, Neiman, Tal, Rizve, Mamshad Nayeem, Tran, Son, Yao, Benjamin, Chilimbi, Trishul, Shah, Mubarak
Recent advancements in Multimodal Large Language Models (MLLMs) have revolutionized the field of vision-language understanding by integrating visual perception capabilities into Large Language Models (LLMs). The prevailing trend in this field involve
Externí odkaz:
http://arxiv.org/abs/2407.13851
Autor:
Gupta, Rohit, Rizve, Mamshad Nayeem, Unnikrishnan, Jayakrishnan, Tawari, Ashish, Tran, Son, Shah, Mubarak, Yao, Benjamin, Chilimbi, Trishul
Pre-trained vision-language models (VLMs) have enabled significant progress in open vocabulary computer vision tasks such as image classification, object detection and image segmentation. Some recent works have focused on extending VLMs to open vocab
Externí odkaz:
http://arxiv.org/abs/2407.09073
Autor:
Do, Phong Nguyen-Thuan, Tran, Son Quoc, Hoang, Phu Gia, Van Nguyen, Kiet, Nguyen, Ngan Luu-Thuy
The success of Natural Language Understanding (NLU) benchmarks in various languages, such as GLUE for English, CLUE for Chinese, KLUE for Korean, and IndoNLU for Indonesian, has facilitated the evaluation of new NLU models across a wide range of task
Externí odkaz:
http://arxiv.org/abs/2403.15882
Autor:
Rizve, Mamshad Nayeem, Fei, Fan, Unnikrishnan, Jayakrishnan, Tran, Son, Yao, Benjamin Z., Zeng, Belinda, Shah, Mubarak, Chilimbi, Trishul
In this paper, we propose VidLA, an approach for video-language alignment at scale. There are two major limitations of previous video-language alignment approaches. First, they do not capture both short-range and long-range temporal dependencies and
Externí odkaz:
http://arxiv.org/abs/2403.14870
Deep learning plays an important role in modern agriculture, especially in plant pathology using leaf images where convolutional neural networks (CNN) are attracting a lot of attention. While numerous reviews have explored the applications of deep le
Externí odkaz:
http://arxiv.org/abs/2310.16273
Publikováno v:
Artificial Intelligence Review 2023
The growing demand for sustainable development brings a series of information technologies to help agriculture production. Especially, the emergence of machine learning applications, a branch of artificial intelligence, has shown multiple breakthroug
Externí odkaz:
http://arxiv.org/abs/2310.12509
The development of large high-quality datasets and high-performing models have led to significant advancements in the domain of Extractive Question Answering (EQA). This progress has sparked considerable interest in exploring unanswerable questions w
Externí odkaz:
http://arxiv.org/abs/2309.05103
Object detection has long been a topic of high interest in computer vision literature. Motivated by the fact that annotating data for the multi-object tracking (MOT) problem is immensely expensive, recent studies have turned their attention to the un
Externí odkaz:
http://arxiv.org/abs/2309.01078