verify-object-claims-cross-query-semantic-spatial-agreement TEC-0230 · proposedVerify object claims by cross-query semantic and spatial agr
For each object claim, query a frozen open-vocabulary detector with four paraphrased prompts, average the top scores for semantic support, score whether the queries' preferred boxes form one overlapping cluster with a dominant candidate, fu
Proposed. Filed by the drafting stage because a paper introduces or tests it. Nobody has vouched for it. The claim drafted from that paper is its first evidence, and what the paper used to validate it is in that claim’s source note. Say whether it held up for you, below.
For each object claim, query a frozen open-vocabulary detector with four paraphrased prompts, average the top scores for semantic support, score whether the queries' preferred boxes form one overlapping cluster with a dominant candidate, fu Filed by the drafting stage from Semantic-Spatial Agreement Verification for Mitigating Object Hallucination in Multimodal Large Language Models (2609.17269) with status proposed: the paper introduces or tests it, and the claim drafted from the paper is its first evidence. The paper validated it by: On LLaVA-1.5-7B, POPE accuracy averaged over three sources rose 1.81 (Popular) and 3.17 (Adversarial) points over vanilla, CHAIRs fell 49.40 to 32.80 and CHAIRi 13.82 to 8.22; a matched-random-deletion control removing the same number of claims only reached CHAIRs 43.40; ablations show single-query 80.20 vs full 81.97 GQA-Adversarial accuracy, and shuffling QIRV evidence drops ROC-AUC 63.32 to 50.43. Random-sampling POPE accuracy fell (90.33 to 88.54) and recall drops as calibration strength grows.. Nobody has vouched for it; add support or contest it on this page.
Does it work?
nothing measured0 supporting · 0 contesting sources
Efficacy claims — what this technique actually moves, under which conditions, and whether that has been contested.
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