5 papers · 1 filter
Rigel: Self-Distilled Score Adaptation for Image and Video Captioning Evaluation
Shuitsu Koyama, Kazuki Matsuda, Yuiga Wada +3
Automatic evaluation of image and video captioning is essential for benchmarking multimodal systems, although standard evaluation metrics show limited alignment with human judgment…
MLLM-as-a-Judge Exhibits Model Preference Bias
Shuitsu Koyama, Yuiga Wada, Daichi Yashima +1
Automatic evaluation using multimodal large language models (MLLMs), commonly referred to as MLLM-as-a-Judge, has been widely used to measure model performance. If such MLLM-as-a-J…
ABMAMBA: Multimodal Large Language Model with Aligned Hierarchical Bidirectional Scan for Efficient Video Captioning
Daichi Yashima, Shuhei Kurita, Yusuke Oda +3
In this study, we focus on video captioning by fully open multimodal large language models (MLLMs). The comprehension of visual sequences is challenging because of their intricate…
NaiLIA: Multimodal Nail Design Retrieval Based on Dense Intent Descriptions and Palette Queries
Kanon Amemiya, Daichi Yashima, Kei Katsumata +4
We focus on the task of retrieving nail design images based on dense intent descriptions, which represent multi-layered user intent for nail designs. This is challenging because su…
ReMoRa: Multimodal Large Language Model based on Refined Motion Representation for Long-Video Understanding
Daichi Yashima, Shuhei Kurita, Yusuke Oda +1
While multimodal large language models (MLLMs) have shown remarkable success across a wide range of tasks, long-form video understanding remains a significant challenge. In this st…