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pro vyhledávání: '"Li Hongsheng"'
Autor:
Ma, Tao, Zhou, Hongbin, Huang, Qiusheng, Yang, Xuemeng, Guo, Jianfei, Zhang, Bo, Dou, Min, Qiao, Yu, Shi, Botian, Li, Hongsheng
Offboard perception aims to automatically generate high-quality 3D labels for autonomous driving (AD) scenes. Existing offboard methods focus on 3D object detection with closed-set taxonomy and fail to match human-level recognition capability on the
Externí odkaz:
http://arxiv.org/abs/2411.05311
Autor:
Dong, Yitong, Li, Yijin, Huang, Zhaoyang, Bian, Weikang, Liu, Jingbo, Bao, Hujun, Cui, Zhaopeng, Li, Hongsheng, Zhang, Guofeng
In this paper, we propose a novel multi-view stereo (MVS) framework that gets rid of the depth range prior. Unlike recent prior-free MVS methods that work in a pair-wise manner, our method simultaneously considers all the source images. Specifically,
Externí odkaz:
http://arxiv.org/abs/2411.01893
Autor:
Li, Yijin, Shen, Yichen, Huang, Zhaoyang, Chen, Shuo, Bian, Weikang, Shi, Xiaoyu, Wang, Fu-Yun, Sun, Keqiang, Bao, Hujun, Cui, Zhaopeng, Zhang, Guofeng, Li, Hongsheng
Recent advances in event-based vision suggest that these systems complement traditional cameras by providing continuous observation without frame rate limitations and a high dynamic range, making them well-suited for correspondence tasks such as opti
Externí odkaz:
http://arxiv.org/abs/2410.20451
Diffusion models achieve superior generation quality but suffer from slow generation speed due to the iterative nature of denoising. In contrast, consistency models, a new generative family, achieve competitive performance with significantly faster s
Externí odkaz:
http://arxiv.org/abs/2410.18958
Autor:
Fang, Rongyao, Duan, Chengqi, Wang, Kun, Li, Hao, Tian, Hao, Zeng, Xingyu, Zhao, Rui, Dai, Jifeng, Li, Hongsheng, Liu, Xihui
Recent advancements in multimodal foundation models have yielded significant progress in vision-language understanding. Initial attempts have also explored the potential of multimodal large language models (MLLMs) for visual content generation. Howev
Externí odkaz:
http://arxiv.org/abs/2410.13861
Medical artificial intelligence (AI) is revolutionizing the interpretation of chest X-ray (CXR) images by providing robust tools for disease diagnosis. However, the effectiveness of these AI models is often limited by their reliance on large amounts
Externí odkaz:
http://arxiv.org/abs/2410.08861
Predicting the future motion of surrounding agents is essential for autonomous vehicles (AVs) to operate safely in dynamic, human-robot-mixed environments. However, the scarcity of large-scale driving datasets has hindered the development of robust a
Externí odkaz:
http://arxiv.org/abs/2410.08669
Autor:
Lu, Zimu, Zhou, Aojun, Wang, Ke, Ren, Houxing, Shi, Weikang, Pan, Junting, Zhan, Mingjie, Li, Hongsheng
Code has been shown to be effective in enhancing the mathematical reasoning abilities of large language models due to its precision and accuracy. Previous works involving continued mathematical pretraining often include code that utilizes math-relate
Externí odkaz:
http://arxiv.org/abs/2410.08196
Autor:
Wang, Guankun, Xiao, Han, Gao, Huxin, Zhang, Renrui, Bai, Long, Yang, Xiaoxiao, Li, Zhen, Li, Hongsheng, Ren, Hongliang
submucosal dissection (ESD) enables rapid resection of large lesions, minimizing recurrence rates and improving long-term overall survival. Despite these advantages, ESD is technically challenging and carries high risks of complications, necessitatin
Externí odkaz:
http://arxiv.org/abs/2410.07540
Rectified Flow Transformers (RFTs) offer superior training and inference efficiency, making them likely the most viable direction for scaling up diffusion models. However, progress in generation resolution has been relatively slow due to data quality
Externí odkaz:
http://arxiv.org/abs/2410.07536