11 papers
CLAP: Direct VLM-to-VLA Adaptation via Language-Action Grounding
Yuri Ishitoya, Jeremy Siburian, Masashi Hamaya +3
Vision-language-action models (VLAs) inherit semantic capabilities from pretrained VLMs, yet large-scale post-training on robot data and architectural modifications can reshape the…
GAVEL: Grounded Caption Error Verification and Localization
Zixian Gao, Atsushi Hashimoto, Kuniaki Saito
Vision-language models (VLMs) often produce hallucinated or inconsistent outputs, where text and images are not properly aligned. Addressing this issue requires not only detecting…
BioVITA: Biological Dataset, Model, and Benchmark for Visual-Textual-Acoustic Alignment
Risa Shinoda, Kaede Shiohara, Nakamasa Inoue +3
Understanding animal species from multimodal data poses an emerging challenge at the intersection of computer vision and ecology. While recent biological models, such as BioCLIP, h…
HalDec-Bench: Benchmarking Hallucination Detector in Image Captioning
Kuniaki Saito, Risa Shinoda, Shohei Tanaka +3
Hallucination detection in captions (HalDec) assesses a vision-language model's ability to correctly align image content with text by identifying errors in captions that misreprese…
HalDec-Bench: Benchmarking Hallucination Detector in Image Captioning
Kuniaki Saito, Risa Shinoda, Shohei Tanaka +3
Hallucination detection in captions (HalDec) assesses a vision-language model's ability to correctly align image content with text by identifying errors in captions that misreprese…
Where-to-Unmask: Ground-Truth-Guided Unmasking Order Learning for Masked Diffusion Language Models
Hikaru Asano, Tadashi Kozuno, Kuniaki Saito +1
Masked Diffusion Language Models (MDLMs) generate text by iteratively filling masked tokens, requiring two coupled decisions at each step: which positions to unmask (where-to-unmas…