<p>As researchers explore potential applications for AI, they have found scenarios where AI could be really useful but there’s not enough data to accurately train the algorithms. Jenq-Neng Hwang, University of Washington professor of electrical and computer and engineering, specializes in these issues. Hwang and his team developed a method that teaches AI to monitor how many distinct poses a baby can achieve throughout the day. The team recently published this work in the IEEE/CVF Winter Conference on Applications of Computer Vision 2024.</p>
I make use of detective work to try and figure out what the alleged AI breakthrough was at OpenAI and has been claimed to be called Q , leading supposedly toward AGI.
Deep learning (DL) based image processing methods have been successfully applied to low-dose x-ray images based on the assumption that the feature distribution of the training data is consistent with that of the test data. However, low-dose computed tomography (LDCT) images from different commercial scanners may contain different amounts and types of image noise, violating this assumption. Moreover, in the application of DL based image processing methods to LDCT, the feature distributions of LDCT images from simulation and clinical CT examination can be quite different. Therefore, the network models trained with simulated image data or LDCT images from one specific scanner may not work well for another CT scanner and image processing task. To solve such domain adaptation problem, in this study, a novel generative adversarial network (GAN) with noise encoding transfer learning (NETL), or GAN-NETL, is proposed to generate a paired dataset with a different noise style. Specifically, we pr
Cross-domain person re-identification (Re-ID) is a challenging and important task in monitoring safety and procedure compliance of industrial work places. In this paper, a novel method is proposed to reduce background induced domain shift for adaptive person Re-ID. Specifically, a foreground-background joint clustering module is proposed to extract discriminative foreground and background features and an attention-based feature disentanglement module is designed to reduce the interference of background with the extraction of discriminative foreground features. Experimental results on three widely used person Re-ID benchmarking datasets (Market-1501, DukeMTMC-reID, and MSMT17) have demonstrated that the proposed method achieves promising performance compared with the state-of-the-art methods.
Society for Science proudly announces the top 300 scholars in the Regeneron Science Talent Search 2021, the nation’s oldest and most prestigious science and math competition for high school seniors.