Zobrazeno 1 - 10
of 33 419
pro vyhledávání: '"Chen, Min"'
In developing machine learning (ML) models for text classification, one common challenge is that the collected data is often not ideally distributed, especially when new classes are introduced in response to changes of data and tasks. In this paper,
Externí odkaz:
http://arxiv.org/abs/2409.15848
Efficient communication can enhance the overall performance of collaborative multi-agent reinforcement learning. A common approach is to share observations through full communication, leading to significant communication overhead. Existing work attem
Externí odkaz:
http://arxiv.org/abs/2409.07127
There is a great need to accurately predict short-term precipitation, which has socioeconomic effects such as agriculture and disaster prevention. Recently, the forecasting models have employed multi-source data as the multi-modality input, thus impr
Externí odkaz:
http://arxiv.org/abs/2409.06732
Autor:
Faure, Gueter Josmy, Yeh, Jia-Fong, Chen, Min-Hung, Su, Hung-Ting, Hsu, Winston H., Lai, Shang-Hong
Existing research often treats long-form videos as extended short videos, leading to several limitations: inadequate capture of long-range dependencies, inefficient processing of redundant information, and failure to extract high-level semantic conce
Externí odkaz:
http://arxiv.org/abs/2408.17443
Spatio-temporal action detection encompasses the tasks of localizing and classifying individual actions within a video. Recent works aim to enhance this process by incorporating interaction modeling, which captures the relationship between people and
Externí odkaz:
http://arxiv.org/abs/2408.15996
Enabling Large Language Models (LLMs) to comprehend the 3D physical world remains a significant challenge. Due to the lack of large-scale 3D-text pair datasets, the success of LLMs has yet to be replicated in 3D understanding. In this paper, we rethi
Externí odkaz:
http://arxiv.org/abs/2408.15966
Autor:
Hirota, Yusuke, Chen, Min-Hung, Wang, Chien-Yi, Nakashima, Yuta, Wang, Yu-Chiang Frank, Hachiuma, Ryo
Large-scale vision-language models, such as CLIP, are known to contain harmful societal bias regarding protected attributes (e.g., gender and age). In this paper, we aim to address the problems of societal bias in CLIP. Although previous studies have
Externí odkaz:
http://arxiv.org/abs/2408.10202
Traditional deep Gaussian processes model the data evolution using a discrete hierarchy, whereas differential Gaussian processes (DIFFGPs) represent the evolution as an infinitely deep Gaussian process. However, prior DIFFGP methods often overlook th
Externí odkaz:
http://arxiv.org/abs/2408.06069
Relativistic polarized electron beams can find applications in broad areas of fundamental physics. Here, we propose for the first time that electron spin polarization can be realized efficiently via collective beam-target interactions. When a relativ
Externí odkaz:
http://arxiv.org/abs/2408.05768