Zobrazeno 1 - 10
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pro vyhledávání: '"Shuchang An"'
Autor:
Meng, Li, Qi, Zhao, Shuchang, Lyu, Chunlei, Wang, Yujing, Ma, Guangliang, Cheng, Chenguang, Yang
Recognizing and grasping novel-category objects remains a crucial yet challenging problem in real-world robotic applications. Despite its significance, limited research has been conducted in this specific domain. To address this, we seamlessly propos
Externí odkaz:
http://arxiv.org/abs/2407.13175
Preconditioned Proximal Point (PPP) algorithms provide a unified framework for splitting methods in image restoration. Recent advancements with RED (Regularization by Denoising) and PnP (Plug-and-Play) priors have achieved state-of-the-art performanc
Externí odkaz:
http://arxiv.org/abs/2407.13120
Accurately predicting watch time is crucial for optimizing recommendations and user experience in short video platforms. However, existing methods that estimate a single average watch time often fail to capture the inherent uncertainty and diversity
Externí odkaz:
http://arxiv.org/abs/2407.12223
Autor:
Fang, Shuangkang, Wang, Yufeng, Tsai, Yi-Hsuan, Yang, Yi, Ding, Wenrui, Zhou, Shuchang, Yang, Ming-Hsuan
Recent work on image content manipulation based on vision-language pre-training models has been effectively extended to text-driven 3D scene editing. However, existing schemes for 3D scene editing still exhibit certain shortcomings, hindering their f
Externí odkaz:
http://arxiv.org/abs/2407.06842
Autor:
Liu, Ziru, Liu, Shuchang, Yang, Bin, Xue, Zhenghai, Cai, Qingpeng, Zhao, Xiangyu, Zhang, Zijian, Hu, Lantao, Li, Han, Jiang, Peng
Recommender systems aim to fulfill the user's daily demands. While most existing research focuses on maximizing the user's engagement with the system, it has recently been pointed out that how frequently the users come back for the service also refle
Externí odkaz:
http://arxiv.org/abs/2406.06043
Autor:
Zhou, Zheng, Zhao, Hongbo, Cheng, Guangliang, Li, Xiangtai, Lyu, Shuchang, Feng, Wenquan, Zhao, Qi
Dataset Distillation (DD) aims to distill knowledge from extensive datasets into more compact ones while preserving performance on the test set, thereby reducing storage costs and training expenses. However, existing methods often suffer from computa
Externí odkaz:
http://arxiv.org/abs/2406.01112
In recent years, AI-Generated Content (AIGC) has witnessed rapid advancements, facilitating the generation of music, images, and other forms of artistic expression across various industries. However, researches on general multi-modal music generation
Externí odkaz:
http://arxiv.org/abs/2405.02801
Autor:
Zhang, Zijian, Liu, Shuchang, Yu, Jiaao, Cai, Qingpeng, Zhao, Xiangyu, Zhang, Chunxu, Liu, Ziru, Liu, Qidong, Zhao, Hongwei, Hu, Lantao, Jiang, Peng, Gai, Kun
Multi-domain recommendation and multi-task recommendation have demonstrated their effectiveness in leveraging common information from different domains and objectives for comprehensive user modeling. Nonetheless, the practical recommendation usually
Externí odkaz:
http://arxiv.org/abs/2404.18465
Autor:
Tao, Shuchang, Yao, Liuyi, Ding, Hanxing, Xie, Yuexiang, Cao, Qi, Sun, Fei, Gao, Jinyang, Shen, Huawei, Ding, Bolin
Despite the success of large language models (LLMs) in natural language generation, much evidence shows that LLMs may produce incorrect or nonsensical text. This limitation highlights the importance of discerning when to trust LLMs, especially in saf
Externí odkaz:
http://arxiv.org/abs/2404.17287
Autor:
Liu, Ziru, Liu, Shuchang, Zhang, Zijian, Cai, Qingpeng, Zhao, Xiangyu, Zhao, Kesen, Hu, Lantao, Jiang, Peng, Gai, Kun
In the landscape of Recommender System (RS) applications, reinforcement learning (RL) has recently emerged as a powerful tool, primarily due to its proficiency in optimizing long-term rewards. Nevertheless, it suffers from instability in the learning
Externí odkaz:
http://arxiv.org/abs/2404.03637