Zobrazeno 1 - 10
of 17
pro vyhledávání: '"Qiao, Limeng"'
Aiming to predict the complete shapes of partially occluded objects, amodal segmentation is an important step towards visual intelligence. With crucial significance, practical prior knowledge derives from sufficient training, while limited amodal ann
Externí odkaz:
http://arxiv.org/abs/2405.16094
Autor:
Chu, Xiangxiang, Qiao, Limeng, Zhang, Xinyu, Xu, Shuang, Wei, Fei, Yang, Yang, Sun, Xiaofei, Hu, Yiming, Lin, Xinyang, Zhang, Bo, Shen, Chunhua
We introduce MobileVLM V2, a family of significantly improved vision language models upon MobileVLM, which proves that a delicate orchestration of novel architectural design, an improved training scheme tailored for mobile VLMs, and rich high-quality
Externí odkaz:
http://arxiv.org/abs/2402.03766
Autor:
Chu, Xiangxiang, Qiao, Limeng, Lin, Xinyang, Xu, Shuang, Yang, Yang, Hu, Yiming, Wei, Fei, Zhang, Xinyu, Zhang, Bo, Wei, Xiaolin, Shen, Chunhua
We present MobileVLM, a competent multimodal vision language model (MMVLM) targeted to run on mobile devices. It is an amalgamation of a myriad of architectural designs and techniques that are mobile-oriented, which comprises a set of language models
Externí odkaz:
http://arxiv.org/abs/2312.16886
Vectorized high-definition map online construction has garnered considerable attention in the field of autonomous driving research. Most existing approaches model changeable map elements using a fixed number of points, or predict local maps in a two-
Externí odkaz:
http://arxiv.org/abs/2308.16477
This report introduces the 1st place winning solution for the Autonomous Driving Challenge 2023 - Online HD-map Construction. By delving into the vectorization pipeline, we elaborate an effective architecture, termed as MachMap, which formulates the
Externí odkaz:
http://arxiv.org/abs/2306.10301
Vectorized high-definition map (HD-map) construction, which focuses on the perception of centimeter-level environmental information, has attracted significant research interest in the autonomous driving community. Most existing approaches first obtai
Externí odkaz:
http://arxiv.org/abs/2306.09700
Visual place retrieval aims to search images in the database that depict similar places as the query image. However, global descriptors encoded by the network usually fall into a low dimensional principal space, which is harmful to the retrieval perf
Externí odkaz:
http://arxiv.org/abs/2204.10972
Few-shot object detection, which aims at detecting novel objects rapidly from extremely few annotated examples of previously unseen classes, has attracted significant research interest in the community. Most existing approaches employ the Faster R-CN
Externí odkaz:
http://arxiv.org/abs/2108.09017
Autor:
Chen, Guangyao, Qiao, Limeng, Shi, Yemin, Peng, Peixi, Li, Jia, Huang, Tiejun, Pu, Shiliang, Tian, Yonghong
Publikováno v:
ECCV 2020
Open set recognition is an emerging research area that aims to simultaneously classify samples from predefined classes and identify the rest as 'unknown'. In this process, one of the key challenges is to reduce the risk of generalizing the inherent c
Externí odkaz:
http://arxiv.org/abs/2011.00178
Few-shot learning, which aims at extracting new concepts rapidly from extremely few examples of novel classes, has been featured into the meta-learning paradigm recently. Yet, the key challenge of how to learn a generalizable classifier with the capa
Externí odkaz:
http://arxiv.org/abs/1910.02224