ReviewJournal of imaging2026
Efficiency and Temporal Reasoning in Transformer-Based Video Object Detection: A Systematic Review.
Review in Journal of imaging, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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.
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.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
This PRISMA 2020 systematic literature review analyzes efficient transformer-based video object detection (VOD). Across five databases through August 2025, 1343 records were identified, with 16 studies meeting the final inclusion criteria after full-text screening. Backward citation tracking added five additional eligible studies, yielding a small final evidence base of 21 studies (22 reports). This review's major contributions are (i) a focused narrative synthesis at the intersection of efficient model design, temporal modeling, and video object detection transformers, due to methodological heterogeneity in the corpus; (ii) ROB-CVA, a proposed risk-of-bias framework for computer vision; and (iii) an evidence-based analysis of temporal modeling strategies and evaluation practices. Furthermore, we examine evaluation inconsistencies, including dataset fragmentation and risk-of-bias. Fifty-two percent of studies were rated as low risk-of-bias using the review-specific, non-validated ROB-CVA framework, driven mainly by non-public code and datasets; this distribution is sensitive to our risk-of-bias aggregation rule. The certainty of evidence is low to moderate. The review outlines open challenges and future directions. This review is registered on OSF (doi: 10.17605/OSF.IO/Q9PJ4), supported by project AEOLUS (PHD IN INDUSTRY/1123/0145), and funded by the Republic of Cyprus through the Research and Innovation Foundation.
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What OpenQuestion holds
Registered trials
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.