Zobrazeno 1 - 10
of 344
pro vyhledávání: '"Xie, Lingxi"'
Autor:
Chen, Pengfei, Xie, Lingxi, Huo, Xinyue, Yu, Xuehui, Zhang, Xiaopeng, Sun, Yingfei, Han, Zhenjun, Tian, Qi
The Segment Anything model (SAM) has shown a generalized ability to group image pixels into patches, but applying it to semantic-aware segmentation still faces major challenges. This paper presents SAM-CP, a simple approach that establishes two types
Externí odkaz:
http://arxiv.org/abs/2407.16682
Autor:
Yi, Taoran, Fang, Jiemin, Zhou, Zanwei, Wang, Junjie, Wu, Guanjun, Xie, Lingxi, Zhang, Xiaopeng, Liu, Wenyu, Wang, Xinggang, Tian, Qi
Recently, 3D Gaussian splatting (3D-GS) has achieved great success in reconstructing and rendering real-world scenes. To transfer the high rendering quality to generation tasks, a series of research works attempt to generate 3D-Gaussian assets from t
Externí odkaz:
http://arxiv.org/abs/2406.18462
Autor:
Liu, Lin, Liu, Quande, Qian, Shengju, Zhou, Yuan, Zhou, Wengang, Li, Houqiang, Xie, Lingxi, Tian, Qi
Video generation is a challenging yet pivotal task in various industries, such as gaming, e-commerce, and advertising. One significant unresolved aspect within T2V is the effective visualization of text within generated videos. Despite the progress a
Externí odkaz:
http://arxiv.org/abs/2406.17777
Autor:
Ma, Tianren, Xie, Lingxi, Tian, Yunjie, Yang, Boyu, Zhang, Yuan, Doermann, David, Ye, Qixiang
An essential topic for multimodal large language models (MLLMs) is aligning vision and language concepts at a finer level. In particular, we devote efforts to encoding visual referential information for tasks such as referring and grounding. Existing
Externí odkaz:
http://arxiv.org/abs/2406.11327
Autor:
Qiu, Jihao, Zhang, Yuan, Tang, Xi, Xie, Lingxi, Ma, Tianren, Yan, Pengyu, Doermann, David, Ye, Qixiang, Tian, Yunjie
Videos carry rich visual information including object description, action, interaction, etc., but the existing multimodal large language models (MLLMs) fell short in referential understanding scenarios such as video-based referring. In this paper, we
Externí odkaz:
http://arxiv.org/abs/2406.00258
Open-vocabulary semantic segmentation seeks to label each pixel in an image with arbitrary text descriptions. Vision-language foundation models, especially CLIP, have recently emerged as powerful tools for acquiring open-vocabulary capabilities. Howe
Externí odkaz:
http://arxiv.org/abs/2405.18840
Autor:
Ge, Jiannan, Xie, Lingxi, Xie, Hongtao, Li, Pandeng, Zhang, Xiaopeng, Zhang, Yongdong, Tian, Qi
A serious issue that harms the performance of zero-shot visual recognition is named objective misalignment, i.e., the learning objective prioritizes improving the recognition accuracy of seen classes rather than unseen classes, while the latter is th
Externí odkaz:
http://arxiv.org/abs/2404.05667
Autor:
Yang, Chen, Li, Sikuang, Fang, Jiemin, Liang, Ruofan, Xie, Lingxi, Zhang, Xiaopeng, Shen, Wei, Tian, Qi
Reconstructing and rendering 3D objects from highly sparse views is of critical importance for promoting applications of 3D vision techniques and improving user experience. However, images from sparse views only contain very limited 3D information, l
Externí odkaz:
http://arxiv.org/abs/2402.10259
Autor:
Tian, Yunjie, Ma, Tianren, Xie, Lingxi, Qiu, Jihao, Tang, Xi, Zhang, Yuan, Jiao, Jianbin, Tian, Qi, Ye, Qixiang
In this study, we establish a baseline for a new task named multimodal multi-round referring and grounding (MRG), opening up a promising direction for instance-level multimodal dialogues. We present a new benchmark and an efficient vision-language mo
Externí odkaz:
http://arxiv.org/abs/2401.13307
Autor:
Liu, Yue, Tian, Yunjie, Zhao, Yuzhong, Yu, Hongtian, Xie, Lingxi, Wang, Yaowei, Ye, Qixiang, Liu, Yunfan
Designing computationally efficient network architectures persists as an ongoing necessity in computer vision. In this paper, we transplant Mamba, a state-space language model, into VMamba, a vision backbone that works in linear time complexity. At t
Externí odkaz:
http://arxiv.org/abs/2401.10166