Zobrazeno 1 - 10
of 148
pro vyhledávání: '"Zeng, Huimin"'
The rapid propagation of misinformation poses substantial risks to public interest. To combat misinformation, large language models (LLMs) are adapted to automatically verify claim credibility. Nevertheless, existing methods heavily rely on the embed
Externí odkaz:
http://arxiv.org/abs/2406.09815
Existing federated learning (FL) studies usually assume the training label space and test label space are identical. However, in real-world applications, this assumption is too ideal to be true. A new user could come up with queries that involve data
Externí odkaz:
http://arxiv.org/abs/2404.01232
The proliferation of online misinformation has posed significant threats to public interest. While numerous online users actively participate in the combat against misinformation, many of such responses can be characterized by the lack of politeness
Externí odkaz:
http://arxiv.org/abs/2403.14952
Federated Recommendation (FR) emerges as a novel paradigm that enables privacy-preserving recommendations. However, traditional FR systems usually represent users/items with discrete identities (IDs), suffering from performance degradation due to the
Externí odkaz:
http://arxiv.org/abs/2403.04256
Autor:
Zheng, Ziqiang, Chen, Yiwei, Zhang, Jipeng, Vu, Tuan-Anh, Zeng, Huimin, Tim, Yue Him Wong, Yeung, Sai-Kit
Large language models (LLMs) have demonstrated a powerful ability to answer various queries as a general-purpose assistant. The continuous multi-modal large language models (MLLM) empower LLMs with the ability to perceive visual signals. The launch o
Externí odkaz:
http://arxiv.org/abs/2401.02147
State-of-the-art sequential recommendation relies heavily on self-attention-based recommender models. Yet such models are computationally expensive and often too slow for real-time recommendation. Furthermore, the self-attention operation is performe
Externí odkaz:
http://arxiv.org/abs/2310.02367
Recent interactive segmentation methods iteratively take source image, user guidance and previously predicted mask as the input without considering the invariant nature of the source image. As a result, extracting features from the source image is re
Externí odkaz:
http://arxiv.org/abs/2308.03529
With emerging online topics as a source for numerous new events, detecting unseen / rare event types presents an elusive challenge for existing event detection methods, where only limited data access is provided for training. To address the data scar
Externí odkaz:
http://arxiv.org/abs/2305.17373
With emerging topics (e.g., COVID-19) on social media as a source for the spreading misinformation, overcoming the distributional shifts between the original training domain (i.e., source domain) and such target domains remains a non-trivial task for
Externí odkaz:
http://arxiv.org/abs/2305.12692
Portrait retouching aims to improve the aesthetic quality of input portrait photos and especially requires human-region priority. The deep learning-based methods largely elevate the retouching efficiency and provide promising retouched results. Howev
Externí odkaz:
http://arxiv.org/abs/2304.04017