6 accepted papers
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 generations; therefore, the neutrality is in question. Most LLM-free metrics do not suffer from such an issue, whereas th
Multimodal Large Language Models (MLLMs) often generate hallucinations, where the output deviates from the visual content. Given that these hallucinations can take diverse forms, detecting hallucinations at a fine-grained level is essential for comprehensive evaluation and analysis. To this end, we
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 metrics for image captioning are primarily designed for short captions and are not suitable for evaluating long captions.
Establishing an automatic evaluation metric that closely aligns with human judgments is essential for effectively developing image captioning models. Recent data-driven metrics have demonstrated a stronger correlation with human judgments than classic metrics such as CIDEr; however they lack suffici…
In this study, we aim to develop a model that comprehends a natural language instruction (e.g., “Go to the living room and get the nearest pillow to the radio art on the wall”) and generates a segmentation mask for the target everyday object. The task is challenging because it requires (1) the under…
For human-safe robot control in human-to-robot handover, the physical properties of containers and fillings should be accurately estimated. In this paper, we propose a Transformer encoder that shares the same architecture and parameters for filling level and type estimation. We also propose a mask-b…