Zobrazeno 1 - 10
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pro vyhledávání: '"A Hwang"'
Autor:
Hwang, Sunhwa, Hwang, Hansub, Kim, Kangjin, Byun, Andrew, Jeong, Seokho, Soegianto, Maynardo Pratama, Ahn, Jaewook
Movable single atoms have drawn significant attention for their potentials as flying quantum memory in non-local, dynamic quantum computing architectures. However, when dynamic optical tweezers are employed to control atoms opto-mechanically, convent
Externí odkaz:
http://arxiv.org/abs/2410.22627
Autor:
On, Jeongwan, Gwak, Kyeonghwan, Kang, Gunyoung, Hwang, Hyein, Hwang, Soohyun, Cha, Junuk, Han, Jaewook, Baek, Seungryul
This report describes our 1st place solution to the 8th HANDS workshop challenge (ARCTIC track) in conjunction with ECCV 2024. In this challenge, we address the task of bimanual category-agnostic hand-object interaction reconstruction, which aims to
Externí odkaz:
http://arxiv.org/abs/2409.19215
As Large Language Models (LLMs) are increasingly deployed in specialized domains with continuously evolving knowledge, the need for timely and precise knowledge injection has become essential. Fine-tuning with paraphrased data is a common approach to
Externí odkaz:
http://arxiv.org/abs/2411.00686
Autor:
Kim, Beomyoung, Shin, Chanyong, Jeong, Joonhyun, Jung, Hyungsik, Lee, Se-Yun, Chun, Sewhan, Hwang, Dong-Hyun, Yu, Joonsang
The recent segmentation foundation model, Segment Anything Model (SAM), exhibits strong zero-shot segmentation capabilities, but it falls short in generating fine-grained precise masks. To address this limitation, we propose a novel zero-shot image m
Externí odkaz:
http://arxiv.org/abs/2411.00626
3D point clouds are increasingly vital for applications like autonomous driving and robotics, yet the raw data captured by sensors often suffer from noise and sparsity, creating challenges for downstream tasks. Consequently, point cloud upsampling be
Externí odkaz:
http://arxiv.org/abs/2411.00432
Autor:
Sohn, Jiwoong, Park, Yein, Yoon, Chanwoong, Park, Sihyeon, Hwang, Hyeon, Sung, Mujeen, Kim, Hyunjae, Kang, Jaewoo
Large language models (LLM) hold significant potential for applications in biomedicine, but they struggle with hallucinations and outdated knowledge. While retrieval-augmented generation (RAG) is generally employed to address these issues, it also ha
Externí odkaz:
http://arxiv.org/abs/2411.00300
Disentangled representation learning (DRL) aims to break down observed data into core intrinsic factors for a profound understanding of the data. In real-world scenarios, manually defining and labeling these factors are non-trivial, making unsupervis
Externí odkaz:
http://arxiv.org/abs/2410.23820
We study the long-standing open question of the power of unique witness in quantum protocols, which asks if UniqueQMA, a variant of QMA whose accepting witness space is 1-dimensional, is equal to QMA. We show a quantum oracle separation between Uniqu
Externí odkaz:
http://arxiv.org/abs/2410.23811
Autor:
Hwang, Jyh-Jing, Xu, Runsheng, Lin, Hubert, Hung, Wei-Chih, Ji, Jingwei, Choi, Kristy, Huang, Di, He, Tong, Covington, Paul, Sapp, Benjamin, Guo, James, Anguelov, Dragomir, Tan, Mingxing
We introduce EMMA, an End-to-end Multimodal Model for Autonomous driving. Built on a multi-modal large language model foundation, EMMA directly maps raw camera sensor data into various driving-specific outputs, including planner trajectories, percept
Externí odkaz:
http://arxiv.org/abs/2410.23262
Retrieval-augmented generation (RAG) addresses key limitations of large language models (LLMs), such as hallucinations and outdated knowledge, by incorporating external databases. These databases typically consult multiple sources to encompass up-to-
Externí odkaz:
http://arxiv.org/abs/2410.22954