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pro vyhledávání: '"Gan, Yukang"'
Vision-Language Models (VLMs) have achieved remarkable success in various multi-modal tasks, but they are often bottlenecked by the limited context window and high computational cost of processing high-resolution image inputs and videos. Vision compr
Externí odkaz:
http://arxiv.org/abs/2406.12275
Autor:
Li, Ang, Xiao, Qiugen, Cao, Peng, Tang, Jian, Yuan, Yi, Zhao, Zijie, Chen, Xiaoyuan, Zhang, Liang, Li, Xiangyang, Yang, Kaitong, Guo, Weidong, Gan, Yukang, Yu, Xu, Wang, Daniell, Shan, Ying
Reinforcement Learning from AI Feedback (RLAIF) has the advantages of shorter annotation cycles and lower costs over Reinforcement Learning from Human Feedback (RLHF), making it highly efficient during the rapid strategy iteration periods of large la
Externí odkaz:
http://arxiv.org/abs/2403.08309
Autor:
Wu, Chengyue, Gan, Yukang, Ge, Yixiao, Lu, Zeyu, Wang, Jiahao, Feng, Ye, Shan, Ying, Luo, Ping
Humans generally acquire new skills without compromising the old; however, the opposite holds for Large Language Models (LLMs), e.g., from LLaMA to CodeLLaMA. To this end, we propose a new post-pretraining method for LLMs with an expansion of Transfo
Externí odkaz:
http://arxiv.org/abs/2401.02415
Autor:
Gan, Yukang, Ge, Yixiao, Zhou, Chang, Su, Shupeng, Xu, Zhouchuan, Xu, Xuyuan, Hui, Quanchao, Chen, Xiang, Wang, Yexin, Shan, Ying
Large-scale embedding-based retrieval (EBR) is the cornerstone of search-related industrial applications. Given a user query, the system of EBR aims to identify relevant information from a large corpus of documents that may be tens or hundreds of bil
Externí odkaz:
http://arxiv.org/abs/2302.08714
Recognizing objects from simultaneously sensed photometric (RGB) and depth channels is a fundamental yet practical problem in many machine vision applications such as robot grasping and autonomous driving. In this paper, we address this problem by de
Externí odkaz:
http://arxiv.org/abs/1810.12829
This paper aims at task-oriented action prediction, i.e., predicting a sequence of actions towards accomplishing a specific task under a certain scene, which is a new problem in computer vision research. The main challenges lie in how to model task-s
Externí odkaz:
http://arxiv.org/abs/1707.04677
Semantic labeling of RGB-D scenes is crucial to many intelligent applications including perceptual robotics. It generates pixelwise and fine-grained label maps from simultaneously sensed photometric (RGB) and depth channels. This paper addresses this
Externí odkaz:
http://arxiv.org/abs/1604.05000
Publikováno v:
Computer Vision – ECCV 2018 ISBN: 9783030012182
ECCV (3)
ECCV (3)
Significant progress has been made in monocular depth estimation with Convolutional Neural Networks (CNNs). While absolute features, such as edges and textures, could be effectively extracted, the depth constraint of neighboring pixels, namely relati
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::d835ed37873d8f69118e1e2dec869e64
https://doi.org/10.1007/978-3-030-01219-9_14
https://doi.org/10.1007/978-3-030-01219-9_14
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