Zobrazeno 1 - 10
of 2 216
pro vyhledávání: '"Chen, Junyu"'
Affine registration plays a crucial role in PET/CT imaging, where aligning PET with CT images is challenging due to their respective functional and anatomical representations. Despite the significant promise shown by recent deep learning (DL)-based m
Externí odkaz:
http://arxiv.org/abs/2409.13863
Autor:
Chen, Junyu, Soh, Yong Sheng
The Quadratic Assignment Problem (QAP) is an important discrete optimization instance that encompasses many well-known combinatorial optimization problems, and has applications in a wide range of areas such as logistics and computer vision. The QAP,
Externí odkaz:
http://arxiv.org/abs/2409.08802
Autor:
Wu, Yecheng, Zhang, Zhuoyang, Chen, Junyu, Tang, Haotian, Li, Dacheng, Fang, Yunhao, Zhu, Ligeng, Xie, Enze, Yin, Hongxu, Yi, Li, Han, Song, Lu, Yao
VILA-U is a Unified foundation model that integrates Video, Image, Language understanding and generation. Traditional visual language models (VLMs) use separate modules for understanding and generating visual content, which can lead to misalignment a
Externí odkaz:
http://arxiv.org/abs/2409.04429
Graph Neural Networks (GNNs) have exhibited remarkable efficacy in learning from multi-view graph data. In the framework of multi-view graph neural networks, a critical challenge lies in effectively combining diverse views, where each view has distin
Externí odkaz:
http://arxiv.org/abs/2408.07331
Autor:
Lu, Jian, Srivastava, Shikhar, Chen, Junyu, Shrestha, Robik, Acharya, Manoj, Kafle, Kushal, Kanan, Christopher
With the advent of multi-modal large language models (MLLMs), datasets used for visual question answering (VQA) and referring expression comprehension have seen a resurgence. However, the most popular datasets used to evaluate MLLMs are some of the e
Externí odkaz:
http://arxiv.org/abs/2408.05334
Deformable image registration establishes non-linear spatial correspondences between fixed and moving images. Deep learning-based deformable registration methods have been widely studied in recent years due to their speed advantage over traditional a
Externí odkaz:
http://arxiv.org/abs/2407.10209
Understanding the uncertainty inherent in deep learning-based image registration models has been an ongoing area of research. Existing methods have been developed to quantify both transformation and appearance uncertainties related to the registratio
Externí odkaz:
http://arxiv.org/abs/2403.05111
Autor:
Bian, Zhangxing, Alshareef, Ahmed, Wei, Shuwen, Chen, Junyu, Wang, Yuli, Woo, Jonghye, Pham, Dzung L., Zhuo, Jiachen, Carass, Aaron, Prince, Jerry L.
Magnetic Resonance Imaging with tagging (tMRI) has long been utilized for quantifying tissue motion and strain during deformation. However, a phenomenon known as tag fading, a gradual decrease in tag visibility over time, often complicates post-proce
Externí odkaz:
http://arxiv.org/abs/2401.17571
This paper presents GenH2R, a framework for learning generalizable vision-based human-to-robot (H2R) handover skills. The goal is to equip robots with the ability to reliably receive objects with unseen geometry handed over by humans in various compl
Externí odkaz:
http://arxiv.org/abs/2401.00929
The Gromov-Wasserstein (GW) distance is a variant of the optimal transport problem that allows one to match objects between incomparable spaces. At its core, the GW distance is specified as the solution of a non-convex quadratic program and is not kn
Externí odkaz:
http://arxiv.org/abs/2312.14572