Zobrazeno 1 - 10
of 3 876
pro vyhledávání: '"CAI, Xin"'
Autor:
Deng, Ken, Guo, Yuanchen, Sun, Jingxiang, Zou, Zixin, Li, Yangguang, Cai, Xin, Cao, Yanpei, Liu, Yebin, Liang, Ding
Modern 3D generation methods can rapidly create shapes from sparse or single views, but their outputs often lack geometric detail due to computational constraints. We present DetailGen3D, a generative approach specifically designed to enhance these g
Externí odkaz:
http://arxiv.org/abs/2411.16820
Reinforcement Learning (RL) empowers agents to acquire various skills by learning from reward signals. Unfortunately, designing high-quality instance-level rewards often demands significant effort. An emerging alternative, RL with delayed reward, foc
Externí odkaz:
http://arxiv.org/abs/2410.20176
Toxicity classification in textual content remains a significant problem. Data with labels from a single annotator fall short of capturing the diversity of human perspectives. Therefore, there is a growing need to incorporate crowdsourced annotations
Externí odkaz:
http://arxiv.org/abs/2410.14894
Lensless cameras offer significant advantages in size, weight, and cost compared to traditional lens-based systems. Without a focusing lens, lensless cameras rely on computational algorithms to recover the scenes from multiplexed measurements. Howeve
Externí odkaz:
http://arxiv.org/abs/2409.17996
Lensless cameras, innovatively replacing traditional lenses for ultra-thin, flat optics, encode light directly onto sensors, producing images that are not immediately recognizable. This compact, lightweight, and cost-effective imaging solution offers
Externí odkaz:
http://arxiv.org/abs/2406.04129
With the rapid advancement of Vision Language Models (VLMs), VLM-based Image Quality Assessment (IQA) seeks to describe image quality linguistically to align with human expression and capture the multifaceted nature of IQA tasks. However, current met
Externí odkaz:
http://arxiv.org/abs/2405.18842
The model inputs play a key role in the performance of the Bayesian optimization approach. In this paper, we investigate the influence of the inputs on the improved predictions of phenomenological nuclear charge radius formulas using an approach comb
Externí odkaz:
http://arxiv.org/abs/2405.12936
In this paper, we investigate an offline reinforcement learning (RL) problem where datasets are collected from two domains. In this scenario, having datasets with domain labels facilitates efficient policy training. However, in practice, the task of
Externí odkaz:
http://arxiv.org/abs/2404.07465
Action recognition, an essential component of computer vision, plays a pivotal role in multiple applications. Despite significant improvements brought by Convolutional Neural Networks (CNNs), these models suffer performance declines when trained with
Externí odkaz:
http://arxiv.org/abs/2404.06741
In Reinforcement Learning (RL), it is commonly assumed that an immediate reward signal is generated for each action taken by the agent, helping the agent maximize cumulative rewards to obtain the optimal policy. However, in many real-world scenarios,
Externí odkaz:
http://arxiv.org/abs/2402.03771