Zobrazeno 1 - 10
of 3 343
pro vyhledávání: '"Zhang,Sai"'
Leveraging real-time eye-tracking, foveated rendering optimizes hardware efficiency and enhances visual quality virtual reality (VR). This approach leverages eye-tracking techniques to determine where the user is looking, allowing the system to rende
Externí odkaz:
http://arxiv.org/abs/2412.10456
Autor:
Zhang, Sai Qian, Li, Ziyun, Guo, Chuan, Mahloujifar, Saeed, Dangwal, Deeksha, Suh, Edward, De Salvo, Barbara, Liu, Chiao
Inverting visual representations within deep neural networks (DNNs) presents a challenging and important problem in the field of security and privacy for deep learning. The main goal is to invert the features of an unidentified target image generated
Externí odkaz:
http://arxiv.org/abs/2412.10448
Autor:
Xiang, Jingyang, Zhang, Sai Qian
Rotating the activation and weight matrices to reduce the influence of outliers in large language models (LLMs) has recently attracted significant attention, particularly in the context of model quantization. Prior studies have shown that in low-prec
Externí odkaz:
http://arxiv.org/abs/2412.00648
Code quality evaluation involves scoring generated code quality based on a reference code for a specific problem statement. Currently, there are two main forms of evaluating code quality: match-based evaluation and execution-based evaluation. The for
Externí odkaz:
http://arxiv.org/abs/2412.00314
Autor:
Hsieh, He-Yen, Li, Ziyun, Zhang, Sai Qian, Ting, Wei-Te Mark, Chang, Kao-Den, De Salvo, Barbara, Liu, Chiao, Kung, H. T.
We present GazeGen, a user interaction system that generates visual content (images and videos) for locations indicated by the user's eye gaze. GazeGen allows intuitive manipulation of visual content by targeting regions of interest with gaze. Using
Externí odkaz:
http://arxiv.org/abs/2411.04335
Autor:
Chen, Lei, Zhang, Sai, Xu, Fangzhou, Xing, Zhenchang, Wan, Liang, Zhang, Xiaowang, Feng, Zhiyong
In the task of code translation, neural network-based models have been shown to frequently produce semantically erroneous code that deviates from the original logic of the source code. This issue persists even with advanced large models. Although a r
Externí odkaz:
http://arxiv.org/abs/2410.22818
Autor:
Augustin, Maximilian, Sarwar, Syed Shakib, Elhoushi, Mostafa, Zhang, Sai Qian, Li, Yuecheng, De Salvo, Barbara
Following their success in natural language processing (NLP), there has been a shift towards transformer models in computer vision. While transformers perform well and offer promising multi-tasking performance, due to their high compute requirements,
Externí odkaz:
http://arxiv.org/abs/2410.17661
Autor:
Zhao, Yiwei, Li, Ziyun, Khwa, Win-San, Sun, Xiaoyu, Zhang, Sai Qian, Sarwar, Syed Shakib, Stangherlin, Kleber Hugo, Lu, Yi-Lun, Gomez, Jorge Tomas, Seo, Jae-Sun, Gibbons, Phillip B., De Salvo, Barbara, Liu, Chiao
Low-Latency and Low-Power Edge AI is essential for Virtual Reality and Augmented Reality applications. Recent advances show that hybrid models, combining convolution layers (CNN) and transformers (ViT), often achieve superior accuracy/performance tra
Externí odkaz:
http://arxiv.org/abs/2410.08326
Identifying and classifying shutdown initiating events (SDIEs) is critical for developing low power shutdown probabilistic risk assessment for nuclear power plants. Existing computational approaches cannot achieve satisfactory performance due to the
Externí odkaz:
http://arxiv.org/abs/2410.00929
Autor:
Dong, Zhenyuan, Zhang, Sai Qian
Diffusion Transformers (DiTs) have recently attracted significant interest from both industry and academia due to their enhanced capabilities in visual generation, surpassing the performance of traditional diffusion models that employ U-Net. However,
Externí odkaz:
http://arxiv.org/abs/2409.07756