atlas

Research map / Detection and segmentation / Open-set and few-shot detection

Tensorize, Factorize and Regularize: Robust Visual Relationship Learning

CVPR 2018 · Detection and segmentation › Open-set and few-shot detection · approach: Visual relationship and scene graph detection · 73 citations (OpenAlex)

Read the paperCheck your idea against 108,967 papers

AI-extracted summary. Written by AI from the paper's title only (no summary yet). Numbers and claims can be wrong or missing context, so check the paper before you cite or build on it.

Measured effects reported in the paper

What was changedWhereMeasuredEffect
Add the tensor-factorization relational prior to visual relationship detectionProposed method · Scene Graph, zero-shot relationship detectionrecall: ∼40%Helped
Add the tensor-factorization relational prior to the scene graph pipelineVisual Genome scene graph prediction · PredClsR@20: at most ∼30%Helped
Add the tensor-factorization relational prior to the scene graph pipelineVisual Genome scene graph prediction · SgGenrecall: ∼10%Helped
Use the relational learning module (ablation)Visual Genome scene graph prediction · PredCls, SgCls, SgGenrecallHelped
Set prior-application probability to θ = 0.2 rather than higher tested values (ablation)Visual Genome scene graph pipeline · Visual GenomerecallHelped

Closest papers by meaning

Writing a paper in this area? scime draft is a free LaTeX editor with AI included, real-time co-authors and a check of your references and venue rules before you submit. It also imports OpenAI Prism projects.

Open scime draft

Get the next scime Dispatch and a monthly field update

Free. We send a confirmation link first; every email has a one-click unsubscribe. Privacy