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Recently, Qwen released CoGenAV, innovating speech recognition technology with the concept of audio-visual synchronization. It effectively addresses the challenge of noise interference in speech recognition. Traditional speech recognition performs poorly in noisy environments, while CoGenAV takes a different approach by learning the temporal alignment relationships among audio-visual-text, building a more robust and generalizable speech representation framework, systematically improving tasks such as speech recognition (VSR/AVSR), speech reconstruction (AVSS/AVSE), and audio-visual synchronization (A').

With the rapid proliferation of digital devices, how to perfectly adapt images and videos to various screen sizes has become an urgent issue to address. A research team from the University of Sharjah in the UAE recently published a study that developed a novel technology using deep learning models to automatically predict the optimal size of images for seamless display across different devices. The core of this research utilizes transfer learning techniques, employing deep learning models such as Resnet18, DenseNet121, and InceptionV3.

Zhang Xiangyu, an outstanding scientist in the field of AI, rose to fame for his involvement in the ResNet research, which innovatively introduced residual connections and is crucial for modern deep learning models. As a member of the post-90s generation, he joined Jietuo Star, sparking industry attention. Zhang's academic achievements include achieving the best historical result for Xi'an Jiaotong University in a mathematical modeling competition and co-completing the ResNet research during his time at Microsoft Research Asia. The ResNet paper has been cited over 230,000 times, making it one of the most popular deep learning works of this century.