Zobrazeno 1 - 10
of 168
pro vyhledávání: '"Xu, Yitao"'
Vision Transformers (ViTs) demonstrate remarkable performance in image classification through visual-token interaction learning, particularly when equipped with local information via region attention or convolutions. Although such architectures impro
Externí odkaz:
http://arxiv.org/abs/2406.08298
Neural Cellular Automata (NCA) models are trainable variations of traditional Cellular Automata (CA). Emergent motion in the patterns created by NCA has been successfully applied to synthesize dynamic textures. However, the conditions required for an
Externí odkaz:
http://arxiv.org/abs/2404.06406
Publikováno v:
Artificial Life (ALife) 2024
Neural Cellular Automata (NCA) is a class of Cellular Automata where the update rule is parameterized by a neural network that can be trained using gradient descent. In this paper, we focus on NCA models used for texture synthesis, where the update r
Externí odkaz:
http://arxiv.org/abs/2404.06279
Autor:
Pajouheshgar, Ehsan, Xu, Yitao, Mordvintsev, Alexander, Niklasson, Eyvind, Zhang, Tong, Süsstrunk, Sabine
Texture modeling and synthesis are essential for enhancing the realism of virtual environments. Methods that directly synthesize textures in 3D offer distinct advantages to the UV-mapping-based methods as they can create seamless textures and align m
Externí odkaz:
http://arxiv.org/abs/2311.02820
Autor:
Xu, Yitao
In recent years, texture synthesis has been a heated topic in computer graphics, and the development of advanced algorithms for generating high-quality 3D textures is an area of active research. A recently proposed model, Neural Cellular Automata, ca
Externí odkaz:
http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-333926
In the realm of multimodal data integration, feature alignment plays a pivotal role. This paper introduces an innovative approach to feature alignment that revolutionizes the fusion of multimodal information. Our method employs a novel iterative proc
Externí odkaz:
http://arxiv.org/abs/2306.16950
Current Dynamic Texture Synthesis (DyTS) models can synthesize realistic videos. However, they require a slow iterative optimization process to synthesize a single fixed-size short video, and they do not offer any post-training control over the synth
Externí odkaz:
http://arxiv.org/abs/2211.11417
Autor:
Balcells, Cristina, Xu, Yitao, Gil-Solsona, Rubén, Maitre, Léa, Gago-Ferrero, Pablo, Keun, Hector C.
Publikováno v:
In Current Opinion in Chemical Biology February 2024 78
Publikováno v:
In Chinese Journal of Aeronautics October 2024
Autor:
Liu, Aishan, Huang, Tairan, Liu, Xianglong, Xu, Yitao, Ma, Yuqing, Chen, Xinyun, Maybank, Stephen J., Tao, Dacheng
Adversarial attacks are valuable for providing insights into the blind-spots of deep learning models and help improve their robustness. Existing work on adversarial attacks have mainly focused on static scenes; however, it remains unclear whether suc
Externí odkaz:
http://arxiv.org/abs/2005.09161