2 code implementations • 8 May 2024 • Zehan Wang, Ziang Zhang, Xize Cheng, Rongjie Huang, Luping Liu, Zhenhui Ye, Haifeng Huang, Yang Zhao, Tao Jin, Peng Gao, Zhou Zhao
In this work, we propose FreeBind, an idea that treats multimodal representation spaces as basic units, and freely augments pre-trained unified space by integrating knowledge from extra expert spaces via "space bonds".
no code implementations • 14 Apr 2024 • Zhiqing Hong, Rongjie Huang, Xize Cheng, Yongqi Wang, RuiQi Li, Fuming You, Zhou Zhao, Zhimeng Zhang
A song is a combination of singing voice and accompaniment.
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.
2 code implementations • 13 Dec 2023 • Haifeng Huang, Zehan Wang, Rongjie Huang, Luping Liu, Xize Cheng, Yang Zhao, Tao Jin, Zhou Zhao
These tokens capture the object's attributes and spatial relationships with surrounding objects in the 3D scene.
no code implementations • 25 Jul 2023 • Zehan Wang, Haifeng Huang, Yang Zhao, Linjun Li, Xize Cheng, Yichen Zhu, Aoxiong Yin, Zhou Zhao
3D visual grounding aims to localize the target object in a 3D point cloud by a free-form language description.
1 code implementation • ICCV 2023 • Zehan Wang, Haifeng Huang, Yang Zhao, Linjun Li, Xize Cheng, Yichen Zhu, Aoxiong Yin, Zhou Zhao
To accomplish this, we design a novel semantic matching model that analyzes the semantic similarity between object proposals and sentences in a coarse-to-fine manner.
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.
no code implementations • 24 May 2023 • Rongjie Huang, Huadai Liu, Xize Cheng, Yi Ren, Linjun Li, Zhenhui Ye, Jinzheng He, Lichao Zhang, Jinglin Liu, Xiang Yin, Zhou Zhao
Direct speech-to-speech translation (S2ST) aims to convert speech from one language into another, and has demonstrated significant progress to date.
no code implementations • NeurIPS 2023 • Zehan Wang, Yang Zhao, Xize Cheng, Haifeng Huang, Jiageng Liu, Li Tang, Linjun Li, Yongqi Wang, Aoxiong Yin, Ziang Zhang, Zhou Zhao
This paper proposes a novel training-efficient method for learning MCR without paired data called Connecting Multi-modal Contrastive Representations (C-MCR).
no code implementations • 21 May 2023 • Huadai Liu, Rongjie Huang, Jinzheng He, Gang Sun, Ran Shen, Xize Cheng, Zhou Zhao
Speech-to-SQL (S2SQL) aims to convert spoken questions into SQL queries given relational databases, which has been traditionally implemented in a cascaded manner while facing the following challenges: 1) model training is faced with the major issue of data scarcity, where limited parallel data is available; and 2) the systems should be robust enough to handle diverse out-of-domain speech samples that differ from the source data.
2 code implementations • ICCV 2023 • Xize Cheng, Linjun Li, Tao Jin, Rongjie Huang, Wang Lin, Zehan Wang, Huangdai Liu, Ye Wang, Aoxiong Yin, Zhou Zhao
However, despite researchers exploring cross-lingual translation techniques such as machine translation and audio speech translation to overcome language barriers, there is still a shortage of cross-lingual studies on visual speech.
no code implementations • ICCV 2023 • Wang Lin, Tao Jin, Ye Wang, Wenwen Pan, Linjun Li, Xize Cheng, Zhou Zhao
In this study, we propose a new task, group video captioning, which aims to infer the desired content among a group of target videos and describe it with another group of related reference videos.
1 code implementation • 21 Nov 2022 • Luping Liu, Yi Ren, Xize Cheng, Rongjie Huang, Chongxuan Li, Zhou Zhao
In this paper, we introduce a new perceptron bias assumption that suggests discriminator models are more sensitive to certain features of the input, leading to the overconfidence problem.