1 code implementation • 11 Mar 2024 • Zihao Tang, Zheqi Lv, Shengyu Zhang, Yifan Zhou, Xinyu Duan, Fei Wu, Kun Kuang
However, simply adopting models derived from DFKD for real-world applications suffers significant performance degradation, due to the discrepancy between teachers' training data and real-world scenarios (student domain).
no code implementations • 5 Mar 2024 • Dong Yao, Asaad Alghamdi, Qingrong Xia, Xiaoye Qu, Xinyu Duan, Zhefeng Wang, Yi Zheng, Baoxing Huai, Peilun Cheng, Zhou Zhao
Although DC-Match is a simple yet effective method for semantic matching, it highly depends on the external NER techniques to identify the keywords of sentences, which limits the performance of semantic matching for minor languages since satisfactory NER tools are usually hard to obtain.
no code implementations • 23 Dec 2023 • Xize Cheng, Rongjie Huang, Linjun Li, Tao Jin, Zehan Wang, Aoxiong Yin, Minglei Li, Xinyu Duan, Changpeng Yang, Zhou Zhao
However, talking head translation, converting audio-visual speech (i. e., talking head video) from one language into another, still confronts several challenges compared to audio speech: (1) Existing methods invariably rely on cascading, synthesizing via both audio and text, resulting in delays and cascading errors.
no code implementations • 17 Dec 2023 • Yu Zhang, Rongjie Huang, RuiQi Li, Jinzheng He, Yan Xia, Feiyang Chen, Xinyu Duan, Baoxing Huai, Zhou Zhao
Moreover, existing SVS methods encounter a decline in the quality of synthesized singing voices in OOD scenarios, as they rest upon the assumption that the target vocal attributes are discernible during the training phase.
1 code implementation • 14 Nov 2023 • Houquan Zhou, Yang Hou, Zhenghua Li, Xuebin Wang, Zhefeng Wang, Xinyu Duan, Min Zhang
While recent advancements in large language models (LLMs) bring us closer to achieving artificial general intelligence, the question persists: Do LLMs truly understand language, or do they merely mimic comprehension through pattern recognition?
1 code implementation • 21 Sep 2023 • Yanggan Gu, Yang Hou, Zhefeng Wang, Xinyu Duan, Zhenghua Li
Compared to their work, we make progress in three aspects: (1) adopting a much more efficient decoding algorithm of $O(n^4)$ time complexity, (2) exploring joint modeling at the training phase, instead of only at the inference phase, (3) proposing high-order scoring components to promote constituent-dependency interaction.
1 code implementation • 28 Aug 2023 • Shengpeng Ji, Jialong Zuo, Minghui Fang, Ziyue Jiang, Feiyang Chen, Xinyu Duan, Baoxing Huai, Zhou Zhao
The dataset comprises 236, 220 pairs of style prompt in natural text descriptions with five style factors and corresponding speech samples.
no code implementations • 11 Jun 2023 • Asaad Alghamdi, Xinyu Duan, Wei Jiang, Zhenhai Wang, Yimeng Wu, Qingrong Xia, Zhefeng Wang, Yi Zheng, Mehdi Rezagholizadeh, Baoxing Huai, Peilun Cheng, Abbas Ghaddar
Developing monolingual large Pre-trained Language Models (PLMs) is shown to be very successful in handling different tasks in Natural Language Processing (NLP).
1 code implementation • 10 Jun 2023 • Xize Cheng, Tao Jin, Linjun Li, Wang Lin, Xinyu Duan, Zhou Zhao
We demonstrate that OpenSR enables modality transfer from one to any in three different settings (zero-, few- and full-shot), and achieves highly competitive zero-shot performance compared to the existing few-shot and full-shot lip-reading methods.
1 code implementation • 21 Feb 2022 • Jiong Wang, Zhou Zhao, Weike Jin, Xinyu Duan, Zhen Lei, Baoxing Huai, Yiling Wu, Xiaofei He
In this paper, the VLAD aggregation method is adopted to quantize local features with visual vocabulary locally partitioning the feature space, and hence preserve the local discriminability.