atlas

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

Relational Learning for Joint Head and Human Detection

AAAI 2020 · Detection and segmentation › Open-set and few-shot detection · approach: Visual relationship and scene graph detection · 53 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
Use a joint head-human framework with body proposals derived from head proposals instead of separate detectorsFPN head and human detection · CrowdHuman validationHead MR^-2: 48.9% to 48.7%Helped
Use a joint head-human framework with body proposals derived from head proposals instead of separate detectorsFPN head and human detection · CrowdHuman validationHuman MR^-2: 49.7% to 47.0%Helped
Use the cascade branch in the separate human detectorFPN-Human · CrowdHuman validationHuman MR^-2: 49.7 -> 49.2Helped
Add the RDM to the joint detector to use head-body relations to remove false head detections (ablation)JointDet · CrowdHuman validationHead MR^-2: 48.7% to 48.3%Helped
Add the RDM to the joint detector to recall bodies suppressed by NMS (ablation)JointDet · CrowdHuman validationHuman MR^-2: 47.0% to 46.5%Helped
Replace separate FPN detection with JointDetPedestrian detector · CityPersons validation ReasonableMR^-2: 11.00% to 10.23%Helped
Use the new re-annotated labels for training instead of refined annotationsFPN · Caltech-USA refined validationMR^-2: 4.31% to 3.26%Helped
Evaluate using new annotations rather than refined annotationsFPN trained with refined annotations · Caltech-USA validationMR^-2: 4.31% to 16.52%Hurt
Evaluate using new annotations rather than refined annotationsFPN trained with new annotations · Caltech-USA validationMR^-2: 3.26% to 14.26%Hurt

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