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Research map / Vision-language and multimodal / Visual question answering

Commonsense in Parts: Mining Part-Whole Relations from the Web and Image Tags

AAAI 2016 · Vision-language and multimodal › Visual question answering · approach: Open-domain multimodal QA · 22 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
add weighted-support and discriminative-strength scoring for assertion rankingPWKB pattern extraction · assertion rankingprecision: 0.55 -> 0.85Helped
use IMS-based head-word disambiguation instead of most-frequent-sensePWKB · argument disambiguationprecision: 0.70 -> 0.80Helped
rank noun phrases by NPMI plus frequency instead of NPMI aloneWordNet extension · noun phrase qualityprecision: 0.25 -> 0.60Helped
add German and Italian corpora for cardinality inference (ablation)PWKB cardinality · cardinalityprecision: 0.61 -> 0.80Helped
add transitivity and inheritance deduction rules (ablation)PWKB · KB sizeassertions: 382K -> 6.75MHelped
add irreflexivity and acyclicity constraint checking (ablation)PWKB · KB consistencyfalse assertions removed: -146KHelped
use Flickr tags instead of object detector for visibility co-occurrencevisibility attribute · 100K Flickr imagespositive matches: ca. 12,000 -> ca. 26,000Helped
replace Yahoo-snippet part-whole relations with PWKBzero-shot image classification · 200 unseen categoriestop-5 accuracy: 23.8% -> 25.5%Helped

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