Zobrazeno 1 - 10
of 17
pro vyhledávání: '"Lu, Xiangju"'
Large Language Models (LLMs) have been well-researched in various long-context tasks. However, the scarcity of high-quality long-context summarization datasets has hindered further advancements in this area. To address this, we introduce CNNSum, a mu
Externí odkaz:
http://arxiv.org/abs/2412.02819
Autor:
Wu, Xiaodong, Wang, Minhao, Liu, Yichen, Shi, Xiaoming, Yan, He, Lu, Xiangju, Zhu, Junmin, Zhang, Wei
As Large Language Models (LLMs) evolve in natural language processing (NLP), their ability to stably follow instructions in long-context inputs has become critical for real-world applications. However, existing benchmarks seldom focus on instruction-
Externí odkaz:
http://arxiv.org/abs/2411.07037
Existing face swap methods rely heavily on large-scale networks for adequate capacity to generate visually plausible results, which inhibits its applications on resource-constraint platforms. In this work, we propose MobileFSGAN, a novel lightweight
Externí odkaz:
http://arxiv.org/abs/2204.08339
Recovering detailed facial geometry from a set of calibrated multi-view images is valuable for its wide range of applications. Traditional multi-view stereo (MVS) methods adopt an optimization-based scheme to regularize the matching cost. Recently, l
Externí odkaz:
http://arxiv.org/abs/2201.01016
Publikováno v:
In Computers & Graphics May 2024 120
Face recognition has advanced considerably with the availability of large-scale labeled datasets. However, how to further improve the performance with the easily accessible unlabeled dataset remains a challenge. In this paper, we propose the novel Un
Externí odkaz:
http://arxiv.org/abs/1910.10896
Recent years have witnessed increasing attention in cartoon media, powered by the strong demands of industrial applications. As the first step to understand this media, cartoon face recognition is a crucial but less-explored task with few datasets pr
Externí odkaz:
http://arxiv.org/abs/1907.13394
Large convolutional neural network models have recently demonstrated impressive performance on video attention prediction. Conventionally, these models are with intensive computation and large memory. To address these issues, we design an extremely l
Externí odkaz:
http://arxiv.org/abs/1904.04449
Autor:
Liu, Yuanliu, Peng, Bo, Shi, Peipei, Yan, He, Zhou, Yong, Han, Bing, Zheng, Yi, Lin, Chao, Jiang, Jianbin, Fan, Yin, Gao, Tingwei, Wang, Ganwen, Liu, Jian, Lu, Xiangju, Xie, Danming
Person identification in the wild is very challenging due to great variation in poses, face quality, clothes, makeup and so on. Traditional research, such as face recognition, person re-identification, and speaker recognition, often focuses on a sing
Externí odkaz:
http://arxiv.org/abs/1811.07548
Publikováno v:
ICMI
In this paper, we present a video-based emotion recognition system submitted to the EmotiW 2016 Challenge. The core module of this system is a hybrid network that combines recurrent neural network (RNN) and 3D convolutional networks (C3D) in a late-f
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::4f33c847ffff78a100555d6e72fceb9a
https://doi.org/10.1145/2993148.2997632
https://doi.org/10.1145/2993148.2997632