YOLOv7 is a new generation of real-time object detector, which provides the most advanced real-time object detection architecture from edge computing to cloud computing.

This project collaborates with the international team to collect a very large-scale Chinese emotional corpus. In terms of technology, the fairness of speech emotion recognition is also discussed to solve social issues that may be encountered regarding the usability of emotion recognition. Among them, it is found that the database annotations are all labeled with the unfair perspective of men and women, which leads to biases in the trained model. In order to solve this problem, there have been preliminary achievements in the technological development of fairness, and will be submitted in the near future.

This project develops a high-performance end-to-end reinforcement learning training platform for autonomous miniature car racing. With this platform, our team won the championship of Amazon DeepRacer, a world autonomous racing competition. In addition, by combining various reinforcement learning algorithms and frameworks, our self-developed autonomous racing platform can operate at a much higher speed, surpassing the performance of Amazon DeepRacer.

Machines need annotations to learn, but human babies learn human languages with almost no annotations. Can machines do the same thing? To allow machines to learn human languages with only observations like human babies, a research team at Taiwan has partnered with the speech research groups in Meta, CMU, MIT, and JHU to develop a brand new self-supervised speech processing evaluation framework, Speech Processing Universal PERformance Benchmark (SUPERB).

This integrated research project follows the Taiwan's 2030 Science & Technology Vision and takes LOHAS community and inclusive technology as the major research direction. We aim to develop trustable AI technologies, and introduce them to future smart services. That will realize the development of human-centric smart technology, and strengthen the governance and application of emerging technologies. The integrated project consists of 7 sub-projects led by PIs from National Taiwan University, National Tsing-Hua Universiy and Academia Sinica and composed of top AI technological teams. These sub-projects are divided into 3 clusters, including machine learning (sub-projects 1 and 2), computer vision (sub-projects 3 and 4), and human-centric computing (sub-projects 5, 6 and 7). We will deal with the issues of bias, fairness, transparency, explainability, traceability, and so on, from the aspects of data collection, technology, and application landing. Each sub-project will implement specific smart services to reflect the benefits and practical applications of the developed technologies. The NTU Joint Research Center for AI Technology and All Vista Healthcare, an AI Innovation Research Center supported by MOST, is responsible for management, planning, and execution of the integrated research project. We will propose a plan that can be generalized and applied to the intelligent service industry.

Textual Advertisement Generator: Given any limited specifics of any product, AI Advertisement Producer can automatically generate tons of top-quality descriptions and advertisements for the product in just one second. And not just one copy is produced. With deep learning and natural language processing technologies learned from millions of existing samples, our AI model can produce various styles of advertisements at the same time for users to select. It will be a big helper or a virtual brainstorming partner for any brands or advertisers to create their advertisements.

Combined with millimeter wave radar detection of chest undulation breathing mode and heart rate, continuous blood oxygen detection, active disease record of chat robot, and mobile phone analysis of 30 second sitting and standing alternate activity frequency mode, a set of personalized respiratory capacity benchmark is established through AI modeling, which can be applied to zero-contact respiratory physiological monitoring and useful for infectious disease ward, epidemic prevention hotels, centralized quarantine centers.

For video anomaly detection, we apply pretrained models to obtain the foreground and the optical flow as ground truth. Then our model estimates the information by taking only a single frame as input. For human behaviors, we take the human poses as input and use a GCN-based model to predict the future poses. Both the anomaly scores of these two works are given by the error of the estimation. For defect detection, our model takes patches of the image as input and learns to extract features. The anomaly score of each patch is given by the distance between the patch and all the training patches.

The dialogue system is the main subsystem of the visually impaired navigation system, which provides destinations for the navigation system through multiple dialogues. We use the knowledge graph as the basis for reasoning. In terms of close-range navigation, deep learning technology is used to develop RGB camera detection depth algorithm, indoor semantic cutting algorithm, integrated detection depth estimation and indoor semantic cutting in indoor obstacle avoidance, etc. The whole system uses the CellS software design framework to integrate distributed AIoT systems.

We provides a wearable device for the visually impaired to walk outdoors. By the deep learning network, the system can recognize and guide the visually impaired to walk on safe areas such as sidewalks and crosswalk. In addition, it can recognize the types of common obstacles and guide the visually impaired to avoid it in advance. Finally, we can convert the Google Maps route into easy-to-understand voice prompts instruction to guide the visually impaired to move in the right direction.