Zobrazeno 1 - 10
of 255
pro vyhledávání: '"Zhang, Tianfang"'
CAS-ViT: Convolutional Additive Self-attention Vision Transformers for Efficient Mobile Applications
Vision Transformers (ViTs) mark a revolutionary advance in neural networks with their token mixer's powerful global context capability. However, the pairwise token affinity and complex matrix operations limit its deployment on resource-constrained sc
Externí odkaz:
http://arxiv.org/abs/2408.03703
Autor:
Chen, Shen, Zhou, Jiale, Jiang, Zhongyu, Zhang, Tianfang, Wu, Zongkai, Hwang, Jenq-Neng, Li, Lei
The creation of high-quality 3D assets is paramount for applications in digital heritage preservation, entertainment, and robotics. Traditionally, this process necessitates skilled professionals and specialized software for the modeling, texturing, a
Externí odkaz:
http://arxiv.org/abs/2407.19035
Autor:
Zhang, TianFang, Doi, Mamoru, Kokubo, Mitsuru, Sako, Shigeyuki, Ohsawa, Ryou, Tominaga, Nozomu, Tanaka, Masaomi, Fukazawa, Yasushi, Takahashi, Hidenori, Arima, Noriaki, Kobayashi, Naoto, Arimatsu, Ko, Okumura, Shin-ichiro, Kondo, Sohei, Kasuga, Toshihiro, Mori, Yuki, Niino, Yuu
We studied the optical variability of 241 BL Lacs and 83 flat-spectrum radio quasars (FSRQ) from the 4LAC catalog using data from the Tomo-e Gozen Northern Sky Transient Survey, with $\sim$ 50 epochs per blazar on average. We excluded blazars whose o
Externí odkaz:
http://arxiv.org/abs/2405.12002
Within the domain of medical analysis, extensive research has explored the potential of mutual learning between Masked Autoencoders(MAEs) and multimodal data. However, the impact of MAEs on intermodality remains a key challenge. We introduce MedFLIP,
Externí odkaz:
http://arxiv.org/abs/2403.04626
Autor:
Li, Lei, Zhang, Tianfang, Jiang, Zhongyu, Yang, Cheng-Yen, Hwang, Jenq-Neng, Oehmcke, Stefan, Gominski, Dimitri Pierre Johannes, Gieseke, Fabian, Igel, Christian
Accurate and consistent methods for counting trees based on remote sensing data are needed to support sustainable forest management, assess climate change mitigation strategies, and build trust in tree carbon credits. Two-dimensional remote sensing i
Externí odkaz:
http://arxiv.org/abs/2403.01932
Autor:
Sui, Yang, Phan, Huy, Xiao, Jinqi, Zhang, Tianfang, Tang, Zijie, Shi, Cong, Wang, Yan, Chen, Yingying, Yuan, Bo
In the exciting generative AI era, the diffusion model has emerged as a very powerful and widely adopted content generation and editing tool for various data modalities, making the study of their potential security risks very necessary and critical.
Externí odkaz:
http://arxiv.org/abs/2402.02739
Deep learning (DL) networks have achieved remarkable performance in infrared small target detection (ISTD). However, these structures exhibit a deficiency in interpretability and are widely regarded as black boxes, as they disregard domain knowledge
Externí odkaz:
http://arxiv.org/abs/2311.00917
Publikováno v:
In Proceedings of the 16th ACM Conference on Security and Privacy in Wireless and Mobile Networks, 2023, ACM, New York, NY, USA, 12 pages
The growing adoption of voice-enabled devices (e.g., smart speakers), particularly in smart home environments, has introduced many security vulnerabilities that pose significant threats to users' privacy and safety. When multiple devices are connecte
Externí odkaz:
http://arxiv.org/abs/2302.02042
The detection of small and medium-sized objects in three dimensions has always been a frontier exploration problem. This technology has a very wide application in sports analysis, games, virtual reality, human animation and other fields. The traditio
Externí odkaz:
http://arxiv.org/abs/2302.00123
Building segmentation from aerial images and 3D laser scanning (LiDAR) is a challenging task due to the diversity of backgrounds, building textures, and image quality. While current research using different types of convolutional and transformer netw
Externí odkaz:
http://arxiv.org/abs/2301.06190