👻 Hallucination Detection¶

🎞️ ECCV2024 · 2 paper notes

BEAF: Observing BEfore-AFter Changes to Evaluate Hallucination in Vision-Language Models: BEAF proposes a "before-after comparison" hallucination evaluation paradigm: by observing changes in VLM responses after removing objects through image editing and introducing four change-aware metrics (TU/IG/SB/ID), it reveals hallucination behaviors that cannot be detected by traditional text-axis evaluations.
LiDAR-Event Stereo Fusion with Hallucinations: This paper proposes the first framework to fuse sparse LiDAR depth points with event stereo cameras. By "hallucinating" (inserting fictitious events) within the event stack representations (VSH) or the raw event stream (BTH), the framework compensates for the missing information of event cameras in motion-free or textureless regions, significantly improving event stereo matching accuracy.