Zobrazeno 1 - 10
of 228
pro vyhledávání: '"Nguyen, Thông"'
Autor:
Nguyen, Thong Quang
This thesis presents two physics analyses using 137 fb−1 proton-proton collision data collected by the CMS experiment at √s = 13 TeV, along with a series of machine-learning solutions to extend the physics program at the LHC and to address the co
To equip artificial intelligence with a comprehensive understanding towards a temporal world, video and 4D panoptic scene graph generation abstracts visual data into nodes to represent entities and edges to capture temporal relations. Existing method
Externí odkaz:
http://arxiv.org/abs/2412.07160
Autor:
Nguyen, Thong Thanh, Bin, Yi, Wu, Xiaobao, Hu, Zhiyuan, Nguyen, Cong-Duy T, Ng, See-Kiong, Luu, Anh Tuan
Temporal grounding, which localizes video moments related to a natural language query, is a core problem of vision-language learning and video understanding. To encode video moments of varying lengths, recent methods employ a multi-level structure kn
Externí odkaz:
http://arxiv.org/abs/2412.07157
We investigate the decay of bosonic dark matter with masses between 1 MeV and 2 TeV into Standard Model final states. We specifically focus on dark photons that kinetically mix with the Standard Model, as well as scalar dark matter models that have Y
Externí odkaz:
http://arxiv.org/abs/2412.00180
Autor:
Nguyen, Thong, Chatterjee, Shubham, MacAvaney, Sean, Mackie, Iain, Dalton, Jeff, Yates, Andrew
Publikováno v:
EMNLP 2024
Learned Sparse Retrieval (LSR) models use vocabularies from pre-trained transformers, which often split entities into nonsensical fragments. Splitting entities can reduce retrieval accuracy and limits the model's ability to incorporate up-to-date wor
Externí odkaz:
http://arxiv.org/abs/2410.07722
Autor:
Nguyen, Thong, Nguyen, Truc-My
Counterfactual statements, which describe events that did not or cannot take place, are beneficial to numerous NLP applications. Hence, we consider the problem of counterfactual detection (CFD) and seek to enhance the CFD models. Previous models are
Externí odkaz:
http://arxiv.org/abs/2409.16668
MAMA: Meta-optimized Angular Margin Contrastive Framework for Video-Language Representation Learning
Autor:
Nguyen, Thong, Bin, Yi, Wu, Xiaobao, Dong, Xinshuai, Hu, Zhiyuan, Le, Khoi, Nguyen, Cong-Duy, Ng, See-Kiong, Tuan, Luu Anh
Data quality stands at the forefront of deciding the effectiveness of video-language representation learning. However, video-text pairs in previous data typically do not align perfectly with each other, which might lead to video-language representati
Externí odkaz:
http://arxiv.org/abs/2407.03788
Dark photons that are sufficiently light and/or weakly-interacting represent a compelling vision of dark matter. Dark photon decay into three photons, which we call the dark photon trident, can be the dominant channel when the dark photon mass falls
Externí odkaz:
http://arxiv.org/abs/2406.19445
Autor:
Nguyen, Thong, Bin, Yi, Xiao, Junbin, Qu, Leigang, Li, Yicong, Wu, Jay Zhangjie, Nguyen, Cong-Duy, Ng, See-Kiong, Tuan, Luu Anh
Humans use multiple senses to comprehend the environment. Vision and language are two of the most vital senses since they allow us to easily communicate our thoughts and perceive the world around us. There has been a lot of interest in creating video
Externí odkaz:
http://arxiv.org/abs/2406.05615
Autor:
Nguyen, Thong Thanh, Hu, Zhiyuan, Wu, Xiaobao, Nguyen, Cong-Duy T, Ng, See-Kiong, Luu, Anh Tuan
Seeking answers effectively for long videos is essential to build video question answering (videoQA) systems. Previous methods adaptively select frames and regions from long videos to save computations. However, this fails to reason over the whole se
Externí odkaz:
http://arxiv.org/abs/2405.19723