Zobrazeno 1 - 10
of 195
pro vyhledávání: '"See, Simon"'
Remarkable advancements in the recolorization of Neural Radiance Fields (NeRF) have simplified the process of modifying NeRF's color attributes. Yet, with the potential of NeRF to serve as shareable digital assets, there's a concern that malicious us
Externí odkaz:
http://arxiv.org/abs/2407.13390
Autor:
Zhou, Xingzhi, Dong, Xin, Li, Chunhao, Bai, Yuning, Xu, Yulong, Cheung, Ka Chun, See, Simon, Song, Xinpeng, Zhang, Runshun, Zhou, Xuezhong, Zhang, Nevin L.
Traditional Chinese medicine (TCM) relies on specific combinations of herbs in prescriptions to treat symptoms and signs, a practice that spans thousands of years. Predicting TCM prescriptions presents a fascinating technical challenge with practical
Externí odkaz:
http://arxiv.org/abs/2407.10510
Neural Radiance Fields (NeRFs) have become a key method for 3D scene representation. With the rising prominence and influence of NeRF, safeguarding its intellectual property has become increasingly important. In this paper, we propose \textbf{NeRFPro
Externí odkaz:
http://arxiv.org/abs/2407.07735
Nutrition estimation is crucial for effective dietary management and overall health and well-being. Existing methods often struggle with sub-optimal accuracy and can be time-consuming. In this paper, we propose NuNet, a transformer-based network desi
Externí odkaz:
http://arxiv.org/abs/2406.01938
Existing Maximum-Entropy (MaxEnt) Reinforcement Learning (RL) methods for continuous action spaces are typically formulated based on actor-critic frameworks and optimized through alternating steps of policy evaluation and policy improvement. In the p
Externí odkaz:
http://arxiv.org/abs/2405.13629
Autor:
Chen, Kuan-Cheng, Li, Tai-Yue, Wang, Yun-Yuan, See, Simon, Wang, Chun-Chieh, Wille, Robert, Chen, Nan-Yow, Yang, An-Cheng, Lin, Chun-Yu
This paper investigates the application of Quantum Support Vector Machines (QSVMs) with an emphasis on the computational advancements enabled by NVIDIA's cuQuantum SDK, especially leveraging the cuTensorNet library. We present a simulation workflow t
Externí odkaz:
http://arxiv.org/abs/2405.02630
Autor:
Wang, Zhaowei, Fan, Wei, Zong, Qing, Zhang, Hongming, Choi, Sehyun, Fang, Tianqing, Liu, Xin, Song, Yangqiu, Wong, Ginny Y., See, Simon
Abstraction ability is crucial in human intelligence, which can also benefit various tasks in NLP study. Existing work shows that LLMs are deficient in abstract ability, and how to improve it remains unexplored. In this work, we design the framework
Externí odkaz:
http://arxiv.org/abs/2402.10646
Autor:
Shi, Xiaoyu, Huang, Zhaoyang, Wang, Fu-Yun, Bian, Weikang, Li, Dasong, Zhang, Yi, Zhang, Manyuan, Cheung, Ka Chun, See, Simon, Qin, Hongwei, Dai, Jifeng, Li, Hongsheng
We introduce Motion-I2V, a novel framework for consistent and controllable image-to-video generation (I2V). In contrast to previous methods that directly learn the complicated image-to-video mapping, Motion-I2V factorizes I2V into two stages with exp
Externí odkaz:
http://arxiv.org/abs/2401.15977
Test-time domain adaptation effectively adjusts the source domain model to accommodate unseen domain shifts in a target domain during inference. However, the model performance can be significantly impaired by continuous distribution changes in the ta
Externí odkaz:
http://arxiv.org/abs/2401.14619
Autor:
Zong, Qing, Wang, Zhaowei, Xu, Baixuan, Zheng, Tianshi, Shi, Haochen, Wang, Weiqi, Song, Yangqiu, Wong, Ginny Y., See, Simon
A main goal of Argument Mining (AM) is to analyze an author's stance. Unlike previous AM datasets focusing only on text, the shared task at the 10th Workshop on Argument Mining introduces a dataset including both text and images. Importantly, these i
Externí odkaz:
http://arxiv.org/abs/2310.05210