Zobrazeno 1 - 10
of 146
pro vyhledávání: '"Fang, Yuwei"'
Autor:
Fang, Yuwei, Menapace, Willi, Siarohin, Aliaksandr, Chen, Tsai-Shien, Wang, Kuan-Chien, Skorokhodov, Ivan, Neubig, Graham, Tulyakov, Sergey
Existing text-to-video diffusion models rely solely on text-only encoders for their pretraining. This limitation stems from the absence of large-scale multimodal prompt video datasets, resulting in a lack of visual grounding and restricting their ver
Externí odkaz:
http://arxiv.org/abs/2407.06304
Autor:
Gu, Jing, Fang, Yuwei, Skorokhodov, Ivan, Wonka, Peter, Du, Xinya, Tulyakov, Sergey, Wang, Xin Eric
Video editing stands as a cornerstone of digital media, from entertainment and education to professional communication. However, previous methods often overlook the necessity of comprehensively understanding both global and local contexts, leading to
Externí odkaz:
http://arxiv.org/abs/2406.12831
We introduce a new architecture for personalization of text-to-image diffusion models, coined Mixture-of-Attention (MoA). Inspired by the Mixture-of-Experts mechanism utilized in large language models (LLMs), MoA distributes the generation workload b
Externí odkaz:
http://arxiv.org/abs/2404.11565
Autor:
Chen, Tsai-Shien, Siarohin, Aliaksandr, Menapace, Willi, Deyneka, Ekaterina, Chao, Hsiang-wei, Jeon, Byung Eun, Fang, Yuwei, Lee, Hsin-Ying, Ren, Jian, Yang, Ming-Hsuan, Tulyakov, Sergey
The quality of the data and annotation upper-bounds the quality of a downstream model. While there exist large text corpora and image-text pairs, high-quality video-text data is much harder to collect. First of all, manual labeling is more time-consu
Externí odkaz:
http://arxiv.org/abs/2402.19479
Autor:
Maharana, Adyasha, Lee, Dong-Ho, Tulyakov, Sergey, Bansal, Mohit, Barbieri, Francesco, Fang, Yuwei
Existing works on long-term open-domain dialogues focus on evaluating model responses within contexts spanning no more than five chat sessions. Despite advancements in long-context large language models (LLMs) and retrieval augmented generation (RAG)
Externí odkaz:
http://arxiv.org/abs/2402.17753
Autor:
Menapace, Willi, Siarohin, Aliaksandr, Skorokhodov, Ivan, Deyneka, Ekaterina, Chen, Tsai-Shien, Kag, Anil, Fang, Yuwei, Stoliar, Aleksei, Ricci, Elisa, Ren, Jian, Tulyakov, Sergey
Contemporary models for generating images show remarkable quality and versatility. Swayed by these advantages, the research community repurposes them to generate videos. Since video content is highly redundant, we argue that naively bringing advances
Externí odkaz:
http://arxiv.org/abs/2402.14797
Autor:
Qian, Guocheng, Cao, Junli, Siarohin, Aliaksandr, Kant, Yash, Wang, Chaoyang, Vasilkovsky, Michael, Lee, Hsin-Ying, Fang, Yuwei, Skorokhodov, Ivan, Zhuang, Peiye, Gilitschenski, Igor, Ren, Jian, Ghanem, Bernard, Aberman, Kfir, Tulyakov, Sergey
We introduce Amortized Text-to-Mesh (AToM), a feed-forward text-to-mesh framework optimized across multiple text prompts simultaneously. In contrast to existing text-to-3D methods that often entail time-consuming per-prompt optimization and commonly
Externí odkaz:
http://arxiv.org/abs/2402.00867
Autor:
Zhang, Zhihan, Lee, Dong-Ho, Fang, Yuwei, Yu, Wenhao, Jia, Mengzhao, Jiang, Meng, Barbieri, Francesco
Instruction tuning has remarkably advanced large language models (LLMs) in understanding and responding to diverse human instructions. Despite the success in high-resource languages, its application in lower-resource ones faces challenges due to the
Externí odkaz:
http://arxiv.org/abs/2311.08711
Autor:
Fang, Yuwei, Khademi, Mahmoud, Zhu, Chenguang, Yang, Ziyi, Pryzant, Reid, Xu, Yichong, Qian, Yao, Yoshioka, Takuya, Yuan, Lu, Zeng, Michael, Huang, Xuedong
Artificial General Intelligence (AGI) requires comprehensive understanding and generation capabilities for a variety of tasks spanning different modalities and functionalities. Integrative AI is one important direction to approach AGI, through combin
Externí odkaz:
http://arxiv.org/abs/2305.13738
Autor:
Yang, Ziyi, Khademi, Mahmoud, Xu, Yichong, Pryzant, Reid, Fang, Yuwei, Zhu, Chenguang, Chen, Dongdong, Qian, Yao, Gao, Mei, Chen, Yi-Ling, Gmyr, Robert, Kanda, Naoyuki, Codella, Noel, Xiao, Bin, Shi, Yu, Yuan, Lu, Yoshioka, Takuya, Zeng, Michael, Huang, Xuedong
The convergence of text, visual, and audio data is a key step towards human-like artificial intelligence, however the current Vision-Language-Speech landscape is dominated by encoder-only models which lack generative abilities. We propose closing thi
Externí odkaz:
http://arxiv.org/abs/2305.12311