4 papers
Minimal Clips, Maximum Salience: Long Video Summarization via Key Moment Extraction
Galann Pennec, Zhengyuan Liu, Nicholas Asher +2
Vision-Language Models (VLMs) are able to process increasingly longer videos. Yet, important visual information is easily lost throughout the entire context and missed by VLMs. Als…
MMA-ASIA: A Multilingual and Multimodal Alignment Framework for Culturally-Grounded Evaluation
Weihua Zheng, Zhengyuan Liu, Tanmoy Chakraborty +32
Large language models (LLMs) are now used worldwide, yet their multimodal understanding and reasoning often degrade outside Western, high-resource settings. We propose MMA-ASIA, a…
BLEnD-Vis: Benchmarking Multimodal Cultural Understanding in Vision Language Models
Bryan Chen Zhengyu Tan, Zheng Weihua, Zhengyuan Liu +4
As vision-language models (VLMs) are deployed globally, their ability to understand culturally situated knowledge becomes essential. Yet, existing evaluations largely assess static…
Integrating Video and Text: A Balanced Approach to Multimodal Summary Generation and Evaluation
Galann Pennec, Zhengyuan Liu, Nicholas Asher +2
Vision-Language Models (VLMs) often struggle to balance visual and textual information when summarizing complex multimodal inputs, such as entire TV show episodes. In this paper, w…