Zobrazeno 1 - 10
of 52
pro vyhledávání: '"MEI, Haiyang"'
Autor:
Xu, Binqian, Shu, Xiangbo, Mei, Haiyang, Xie, Guosen, Fernando, Basura, Shou, Mike Zheng, Tang, Jinhui
Multimodal Large Language Models (MLLMs) have made significant advancements, demonstrating powerful capabilities in processing and understanding multimodal data. Fine-tuning MLLMs with Federated Learning (FL) allows for expanding the training data sc
Externí odkaz:
http://arxiv.org/abs/2411.14717
Autor:
Bai, Zechen, He, Tong, Mei, Haiyang, Wang, Pichao, Gao, Ziteng, Chen, Joya, Liu, Lei, Zhang, Zheng, Shou, Mike Zheng
We introduce VideoLISA, a video-based multimodal large language model designed to tackle the problem of language-instructed reasoning segmentation in videos. Leveraging the reasoning capabilities and world knowledge of large language models, and augm
Externí odkaz:
http://arxiv.org/abs/2409.19603
Autor:
Wang, Yang, Mei, Haiyang, Bao, Qirui, Wei, Ziqi, Shou, Mike Zheng, Li, Haizhou, Dong, Bo, Yang, Xin
We introduce a novel multimodality synergistic knowledge distillation scheme tailored for efficient single-eye motion recognition tasks. This method allows a lightweight, unimodal student spiking neural network (SNN) to extract rich knowledge from an
Externí odkaz:
http://arxiv.org/abs/2407.09521
Recent advancements in large vision-language models (LVLMs) have demonstrated impressive capability in visual information understanding with human language. Despite these advances, LVLMs still face challenges with multimodal hallucination, such as ge
Externí odkaz:
http://arxiv.org/abs/2402.01345
Car detection is an important task that serves as a crucial prerequisite for many automated driving functions. The large variations in lighting/weather conditions and vehicle densities of the scenes pose significant challenges to existing car detecti
Externí odkaz:
http://arxiv.org/abs/2401.02606
Autonomous obstacle avoidance is of vital importance for an intelligent agent such as a mobile robot to navigate in its environment. Existing state-of-the-art methods train a spiking neural network (SNN) with deep reinforcement learning (DRL) to achi
Externí odkaz:
http://arxiv.org/abs/2310.02361
Glass is very common in our daily life. Existing computer vision systems neglect it and thus may have severe consequences, e.g., a robot may crash into a glass wall. However, sensing the presence of glass is not straightforward. The key challenge is
Externí odkaz:
http://arxiv.org/abs/2209.04639
Glass is very common in the real world. Influenced by the uncertainty about the glass region and the varying complex scenes behind the glass, the existence of glass poses severe challenges to many computer vision tasks, making glass segmentation as a
Externí odkaz:
http://arxiv.org/abs/2209.02280
Autor:
Zhang, Jiqing, Long, Chengjiang, Wang, Yuxin, Piao, Haiyin, Mei, Haiyang, Yang, Xin, Yin, Baocai
Recently, deep convolutional neural networks (CNNs) have been widely explored in single image super-resolution (SISR) and contribute remarkable progress. However, most of the existing CNNs-based SISR methods do not adequately explore contextual infor
Externí odkaz:
http://arxiv.org/abs/2104.10488
Camouflaged object segmentation (COS) aims to identify objects that are "perfectly" assimilate into their surroundings, which has a wide range of valuable applications. The key challenge of COS is that there exist high intrinsic similarities between
Externí odkaz:
http://arxiv.org/abs/2104.10475