Pith. sign in

REVIEW 1 cited by

VQD: Visual Query Detection in Natural Scenes

Not yet reviewed by Pith; the record is open.

Re-run · record.json Download PDF Read on arXiv ↗

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 1904.02794 v2 pith:GUHLFAWO submitted 2019-04-04 cs.CV

Manoj Acharya , Karan Jariwala , Christopher Kanan This is my paper · ORCID
classification cs.CV
keywords visualtaskdetectionexpressionlocalizenaturalproposequery
verification ladder T0 review T1 audit T2 compute T3 formal

Signed reviews

No signed human review yet.

0 comments
read the original abstract

We propose Visual Query Detection (VQD), a new visual grounding task. In VQD, a system is guided by natural language to localize a variable number of objects in an image. VQD is related to visual referring expression recognition, where the task is to localize only one object. We describe the first dataset for VQD and we propose baseline algorithms that demonstrate the difficulty of the task compared to referring expression recognition.

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Calibrating Uncertainty Quantification of Multi-Modal LLMs using Grounding

    cs.CL 2025-04 conditional novelty 6.0 of 10

    Combining self-consistency scores with a grounding model's confidence, scaled by a fitted power and offset, reduces expected calibration error for LLaVA and LLaVA-Med on VQAv2 and Slake.

Pith tools