3 papers
cs.AI2026
D2E: Scaling Vision-Action Pretraining on Desktop Data for Transfer to Embodied AI
Suhwan Choi, Jaeyoon Jung, Haebin Seong +7
Large language models leverage internet-scale text data, yet embodied AI remains constrained by the prohibitive costs of physical trajectory collection. Desktop environments -- par…
cs.CV2025
HalLoc: Token-level Localization of Hallucinations for Vision Language Models
Eunkyu Park, Minyeong Kim, Gunhee Kim
Hallucinations pose a significant challenge to the reliability of large vision-language models, making their detection essential for ensuring accuracy in critical applications. Cur…
cs.CV2024
ESREAL: Exploiting Semantic Reconstruction to Mitigate Hallucinations in Vision-Language Models
Minchan Kim, Minyeong Kim, Junik Bae +3
Hallucinations in vision-language models pose a significant challenge to their reliability, particularly in the generation of long captions. Current methods fall short of accuratel…