HKU 213th Congregation – Faculty of Business and Economics (Winter Session) Highlights

HKU 213th Congregation – Faculty of Business and Economics (Winter Session) HighlightsHKU 213th Congregation – Faculty of Business and Economics (Winter Session) HighlightsHKU 213th Congregation – Faculty of Business and Economics (Winter Session) HighlightsHKU 213th Congregation – Faculty of Business and Economics (Winter Session) HighlightsHKU 213th Congregation – Faculty of Business and Economics (Winter Session) HighlightsHKU 213th Congregation – Faculty of Business and Economics (Winter Session) Highlights
VIEW MORE

 

The 213th Congregation Ceremony of the HKU Business School took place at the Grand Hall, Centennial Campus, The University of Hong Kong on December 4 and 5, 2024. This momentous event spanned six sessions, creating a truly special occasion for all involved.

One of the highlights was an inspiring speech delivered by Dean Professor Hongbin Cai. As the graduands entered the next chapter of your life, Professor Cai reminded them: “Embrace the unknown, take calculated risks, and step outside your comfort zones. Opportunities are often found where others see only uncertainty.”

The Votes of Thanks were delivered by graduand representatives Lei Fu, Wei Wang, Xiaona Peng, Daryl Byron Wong, Pinshu Wen and Xinye Liu. They expressed heartfelt gratitude to the faculty, staff, families, and friends for their unwavering support throughout the academic journey. Their words resonated with the audience, capturing the essence of the challenges faced, the triumphs achieved, and the optimistic, forward-looking mindset of the graduates as they embrace the opportunities ahead.

The Congregation Ceremony was attended by over 3,500 people, including more than 1,500 graduands embarking on the next chapter of their lives and 2,000 families and friends who came to support and celebrate this remarkable milestone.

Other Events
HKU Business School Releases a Comprehensive Evaluation Report on the Image-Generation Capabilities of AI Models
2025 | News
HKU Business School Releases a Comprehensive Evaluation Report on the Image-Generation Capabilities of AI Models
HKU Business School released a Comprehensive Evaluation Report on the Image Generation Capabilities of Artificial Intelligence Models, providing a systematic assessment of 15 text-to-image models and 7 multimodal large language models (LLMs). The results showed that ByteDance’s Dreamina and Doubao, as well as Baidu’s ERNIE Bot ranked among the top performers in terms of image content quality for new-image generation and image revision. However, despite DeepSeek having attracted global attention, its newly released text-to-image model, Janus-Pro, did not perform as well in new-image generation. HKU Business School researchers also found that while some text-to-image models excelled in content quality, their performance in safety and responsibility was significantly lacking. In general, multimodal LLMs demonstrated better overall performance compared to text-to-image models.
AI Image Generation Evaluation Results Released: ByteDance and Baidu Perform Well, DeepSeek Janus-Pro Falls Short
2025 | Research
AI Image Generation Evaluation Results Released: ByteDance and Baidu Perform Well, DeepSeek Janus-Pro Falls Short
The frontier of AI models has evolved beyond text processing to encompass the ability to understand and generate visual content. These models not only comprehend images but also generate visual content based on textual prompts. This study presents a systematic evaluation of the image generation capabilities of AI models, focusing on two core tasks: generating new images and revising existing images. Using carefully curated multidimensional test sets, we conducted a comprehensive evaluation of 22 AI models with image generation capabilities, including 15 text-to-image models and 7 multimodal large language models. The results show that ByteDance’s Dreamina and Doubao, as well as Baidu’s ERNIE Bot, demonstrate impressive performance in both new image generation and image revision tasks. Overall, multimodal large language models deliver superior performance compared to text-to-image models.