Zobrazeno 1 - 10
of 963
pro vyhledávání: '"Sun, Mingyang"'
Autor:
Li, Mingcheng, Yang, Dingkang, Liu, Yang, Wang, Shunli, Chen, Jiawei, Wang, Shuaibing, Wei, Jinjie, Jiang, Yue, Xu, Qingyao, Hou, Xiaolu, Sun, Mingyang, Qian, Ziyun, Kou, Dongliang, Zhang, Lihua
Multimodal Sentiment Analysis (MSA) is an important research area that aims to understand and recognize human sentiment through multiple modalities. The complementary information provided by multimodal fusion promotes better sentiment analysis compar
Externí odkaz:
http://arxiv.org/abs/2411.02793
The offline-to-online (O2O) paradigm in reinforcement learning (RL) utilizes pre-trained models on offline datasets for subsequent online fine-tuning. However, conventional O2O RL algorithms typically require maintaining and retraining the large offl
Externí odkaz:
http://arxiv.org/abs/2410.18626
Autor:
Sun, Mingyang, Kou, Dongliang, Yuan, Ruisheng, Yang, Dingkang, Zhai, Peng, Zhao, Xiao, Jiang, Yang, Li, Xiong, Li, Jingchen, Zhang, Lihua
In virtual Hand-Object Interaction (HOI) scenarios, the authenticity of the hand's deformation is important to immersive experience, such as natural manipulation or tactile feedback. Unrealistic deformation arises from simplified hand geometry, negle
Externí odkaz:
http://arxiv.org/abs/2409.05143
Autor:
Zhao, Xiao, Zhang, Xukun, Yang, Dingkang, Sun, Mingyang, Li, Mingcheng, Wang, Shunli, Zhang, Lihua
Accurate and robust multimodal multi-task perception is crucial for modern autonomous driving systems. However, current multimodal perception research follows independent paradigms designed for specific perception tasks, leading to a lack of compleme
Externí odkaz:
http://arxiv.org/abs/2408.09122
Autor:
Zhao, Xiao, Chen, Bo, Sun, Mingyang, Yang, Dingkang, Wang, Youxing, Zhang, Xukun, Li, Mingcheng, Kou, Dongliang, Wei, Xiaoyi, Zhang, Lihua
Vision-based 3D semantic scene completion (SSC) describes autonomous driving scenes through 3D volume representations. However, the occlusion of invisible voxels by scene surfaces poses challenges to current SSC methods in hallucinating refined 3D ge
Externí odkaz:
http://arxiv.org/abs/2408.09104
Autor:
Li, Mingcheng, Yang, Dingkang, Zhao, Xiao, Wang, Shuaibing, Wang, Yan, Yang, Kun, Sun, Mingyang, Kou, Dongliang, Qian, Ziyun, Zhang, Lihua
Multimodal sentiment analysis (MSA) aims to understand human sentiment through multimodal data. Most MSA efforts are based on the assumption of modality completeness. However, in real-world applications, some practical factors cause uncertain modalit
Externí odkaz:
http://arxiv.org/abs/2404.16456
Binary code similarity detection (BCSD) is a fundamental technique for various application. Many BCSD solutions have been proposed recently, which mostly are embedding-based, but have shown limited accuracy and efficiency especially when the volume o
Externí odkaz:
http://arxiv.org/abs/2402.18818
Autor:
Wang, Hao, Gao, Zeyu, Zhang, Chao, Sha, Zihan, Sun, Mingyang, Zhou, Yuchen, Zhu, Wenyu, Sun, Wenju, Qiu, Han, Xiao, Xi
Binary code representation learning has shown significant performance in binary analysis tasks. But existing solutions often have poor transferability, particularly in few-shot and zero-shot scenarios where few or no training samples are available fo
Externí odkaz:
http://arxiv.org/abs/2402.16928
Autor:
Li, Ran, Zhang, Haipeng, Sun, Mingyang, Teng, Fei, Wan, Can, Pineda, Salvador, Kariniotakis, Georges
Better forecasts may not lead to better decision-making. To address this challenge, decision-oriented learning (DOL) has been proposed as a new branch of machine learning that replaces traditional statistical loss with a decision loss to form an end-
Externí odkaz:
http://arxiv.org/abs/2401.03680
Autor:
Hou, Yaqing, Sun, Mingyang, Gupta, Abhishek, Jin, Yaochu, Piao, Haiyin, Ge, Hongwei, Zhang, Qiang
In this paper, we scale evolutionary algorithms to high-dimensional optimization problems that deceptively possess a low effective dimensionality (certain dimensions do not significantly affect the objective function). To this end, an instantiation o
Externí odkaz:
http://arxiv.org/abs/2401.00168