5 papers
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…
LLM-Free Image Captioning Evaluation in Reference-Flexible Settings
Shinnosuke Hirano, Yuiga Wada, Kazuki Matsuda +2
We focus on the automatic evaluation of image captions in both reference-based and reference-free settings. Existing metrics based on large language models (LLMs) favor their own g…
VELA: An LLM-Hybrid-as-a-Judge Approach for Evaluating Long Image Captions
Kazuki Matsuda, Yuiga Wada, Shinnosuke Hirano +2
In this study, we focus on the automatic evaluation of long and detailed image captions generated by multimodal Large Language Models (MLLMs). Most existing automatic evaluation me…
Task Success Prediction for Open-Vocabulary Manipulation Based on Multi-Level Aligned Representations
Miyu Goko, Motonari Kambara, Daichi Saito +2
In this study, we consider the problem of predicting task success for open-vocabulary manipulation by a manipulator, based on instruction sentences and egocentric images before and…