Evidence map›Paper›PMID 37769256›Full record

ArticleProceedings of the National Academy of Sciences of the United States of America2023

Human-like scene interpretation by a guided counterstream processing.

Shimon Ullman, Liav Assif, Alona Strugatski, Ben-Zion Vatashsky, Hila Levi, Aviv Netanyahu, Adam Yaari

Abstract read
In one paragraph

Article in Proceedings of the National Academy of Sciences of the United States of America, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
  2. Human-like scene interpretation by a guided counterstream processing.Proceedings of the National Academy of Sciences of the United States of America · 2023
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Shimon UllmanDepartment of Computer Science, the Weizmann Institute of Science, Rehovot 76100, Israel.ORCID 0000-0003-4331-298X
Liav Assif *Department of Computer Science, the Weizmann Institute of Science, Rehovot 76100, Israel.
Alona Strugatski *Department of Computer Science, the Weizmann Institute of Science, Rehovot 76100, Israel.
Ben-Zion VatashskyDepartment of Computer Science, the Weizmann Institute of Science, Rehovot 76100, Israel.
Hila LeviDepartment of Computer Science, the Weizmann Institute of Science, Rehovot 76100, Israel.
Aviv NetanyahuComputer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA 02139.ORCID 0009-0001-0712-743X
Adam YaariComputer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA 02139.ORCID 0000-0002-1703-9097

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In modeling vision, there has been a remarkable progress in recognizing a range of scene components, but the problem of analyzing full scenes, an ultimate goal of visual perception, is still largely open. To deal with complete scenes, recent work focused on the training of models for extracting the full graph-like structure of a scene. In contrast with scene graphs, humans' scene perception focuses on selected structures in the scene, starting with a limited interpretation and evolving sequentially in a goal-directed manner [G. L. Malcolm, I. I. A. Groen, C. I. Baker,

Indexed as

MotivationVisual PerceptionHumansPattern Recognition, VisualPhotic Stimulationcombinatorial generalizationguided visionscene perceptionscene understandingtop–down processing

Identifiers

PMID37769256
PMCPMC10556630

What OpenQuestion holds

Textmetadata
LicenceCC BY
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.