Zobrazeno 1 - 10
of 448
pro vyhledávání: '"Guo, Tianyu"'
Autor:
Zuo, Jialong, Nie, Ying, Zhou, Hanyu, Zhang, Huaxin, Wang, Haoyu, Guo, Tianyu, Sang, Nong, Gao, Changxin
Recent researches have proven that pre-training on large-scale person images extracted from internet videos is an effective way in learning better representations for person re-identification. However, these researches are mostly confined to pre-trai
Externí odkaz:
http://arxiv.org/abs/2409.18569
Autor:
Nie, Ying, Yan, Binwei, Guo, Tianyu, Liu, Hao, Wang, Haoyu, He, Wei, Zheng, Binfan, Wang, Weihao, Li, Qiang, Sun, Weijian, Wang, Yunhe, Tao, Dacheng
Large language models (LLMs) have achieved remarkable performance on various NLP tasks, yet their potential in more challenging and domain-specific task, such as finance, has not been fully explored. In this paper, we present CFinBench: a meticulousl
Externí odkaz:
http://arxiv.org/abs/2407.02301
In this paper, we introduce SemiRES, a semi-supervised framework that effectively leverages a combination of labeled and unlabeled data to perform RES. A significant hurdle in applying semi-supervised techniques to RES is the prevalence of noisy pseu
Externí odkaz:
http://arxiv.org/abs/2406.01451
Collaboration between different data centers is often challenged by heterogeneity across sites. To account for the heterogeneity, the state-of-the-art method is to re-weight the covariate distributions in each site to match the distribution of the ta
Externí odkaz:
http://arxiv.org/abs/2404.15746
With the advancement of generative modeling techniques, synthetic human speech becomes increasingly indistinguishable from real, and tricky challenges are elicited for the audio deepfake detection (ADD) system. In this paper, we exploit audio feature
Externí odkaz:
http://arxiv.org/abs/2403.01960
Large language models (LLMs) face a daunting challenge due to the excessive computational and memory requirements of the commonly used Transformer architecture. While state space model (SSM) is a new type of foundational network architecture offering
Externí odkaz:
http://arxiv.org/abs/2403.00818
Autor:
Wang, Yunhe, Chen, Hanting, Tang, Yehui, Guo, Tianyu, Han, Kai, Nie, Ying, Wang, Xutao, Hu, Hailin, Bai, Zheyuan, Wang, Yun, Liu, Fangcheng, Liu, Zhicheng, Guo, Jianyuan, Zeng, Sinan, Zhang, Yinchen, Xu, Qinghua, Liu, Qun, Yao, Jun, Xu, Chao, Tao, Dacheng
The recent trend of large language models (LLMs) is to increase the scale of both model size (\aka the number of parameters) and dataset to achieve better generative ability, which is definitely proved by a lot of work such as the famous GPT and Llam
Externí odkaz:
http://arxiv.org/abs/2312.17276
Autor:
Zuo, Jialong, Zhou, Hanyu, Nie, Ying, Zhang, Feng, Guo, Tianyu, Sang, Nong, Wang, Yunhe, Gao, Changxin
Existing text-based person retrieval datasets often have relatively coarse-grained text annotations. This hinders the model to comprehend the fine-grained semantics of query texts in real scenarios. To address this problem, we contribute a new benchm
Externí odkaz:
http://arxiv.org/abs/2312.03441
Vision-language pre-training like CLIP has shown promising performance on various downstream tasks such as zero-shot image classification and image-text retrieval. Most of the existing CLIP-alike works usually adopt relatively large image encoders li
Externí odkaz:
http://arxiv.org/abs/2312.00674
Data-Free Knowledge Distillation (DFKD) plays a vital role in compressing the model when original training data is unavailable. Previous works for DFKD in NLP mainly focus on distilling encoder-only structures like BERT on classification tasks, which
Externí odkaz:
http://arxiv.org/abs/2311.01689