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Semantic-Spatial Agreement Verification for Mitigating Object Hallucination in Multimodal Large Language Models

Paper recorded by Signals 4 on 2026-09-15 in cs.CV. Abstract reproduced from arXiv; link to the original below.

Published 2026-09-15 on arXiv · recorded by Signals 4 on 2026-09-16

Category: cs.CV · 计算机视觉 · first seen 2026-09-16

Abstract

Multimodal large language models generate natural-language responses from visual inputs, yet may mention objects absent from an image. In medication assistance, accessible perception, and environmental decision-making, such hallucinations can create real-world safety risks. We propose Semantic-Spatial Agreement Verification (SSAV), a training-free method for verifying object claims. A visually gro

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#36 most recent of 237 cs.CV papers we have recorded · ↑ newer: Optical-Flow Wingbeat Counting in MuJoCo: A Comparison of Convolutiona · ↓ older: Exploring 2D backbone effects for indoor semantic occupancy prediction
Cite this page: Semantic-Spatial Agreement Verification for Mitigating Object Hallucination in Multimodal Large Language Models: the #36 most recent of 237 cs.CV papers we have recorded (as of 2026-09-15). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/semantic-spatial-agreement-verification-for-mitigating-object-hallucination-in-m.html
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