Blog(1)
Zero-shot sketch-based image retrieval (ZS-SBIR) is a central problem to sketch understanding [6]. This paper aims to tackle all problems associated with the current status quo for ZS-SBIR, including category-level (standard) [4], fine-grained [1], and cross-dataset [3].
Research Areas(0)
Publications(13)
GePSAn: Generative Procedure Step Anticipation in Cooking Videos
AuthorMohamed Ashraf Abdelsalam, Samrudhdhi B. Rangrej, Isma Hadji, Nikita Dvornik, Konstantinos G. Derpanis, Afsaneh Fazly
PublishedInternational Conference on Computer Vision (ICCV)
Date2023-10-02
ESC: Exploration with SoF Commonsense Constraints for Zero-shot Object Navigation
AuthorYilin Shen,Hongxia Jin
PublishedInternational Conference on Machine Learning (ICML)
Date2023-07-29
Genie: Show Me the Data for Quantization
AuthorYongkweon Jeon, Chungman Lee, Ho-young Kim
PublishedComputer Vision and Pattern Recognition(CVPR)
Date2023-06-18
News(4)
Recently, personalized AI systems have gained significant attention. In the TTS field, zero-shot text-to-speech (ZS-TTS) systems [1-7] enable users to create their own TTS systems that replicate their voices with just one utterance, without further training.
Large Language Models (LLMs) have showcased impressive capabilities in text generation, translation, and code synthesis. Recent efforts focus on integrating LLMs, notably ChatGPT, into robotics for tasks like zero-shot system planning [1].
In recent years, text-to-speech (TTS) has accomplished remarkable improvement with the emergence of various end-to end TTS models [1, 2, 3]. Through these advanced models, TTS expands its field from a model built with a professional voice actor to a personalized TTS.
Others(0)