ArticleIEEE transactions on artificial intelligence2023
JSSE: Joint Sequential Semantic Encoder for Zero-Shot Event Recognition.
Article in IEEE transactions on artificial intelligence, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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
1 citing paper in PubMed.
- AI-derived constrained conditional model for screening marker genes through integrated high-throughput transcriptome big data.BMC medical research methodology · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
Abstract
Zero-shot learning (ZSL) is a paradigm in transfer learning that aims to recognize unknown categories by having a mere description of them. The problem of ZSL has been thoroughly studied in the domain of static object recognition, however, ZSL for dynamic events (ZSER) such as activities and gestures has hardly been investigated. In this context, this paper addresses ZSER by relying on semantic attributes of events to transfer the learned knowledge from seen classes to unseen ones. First, we utilized the Amazon Mechanical Turk platform to create the first attribute-based gesture dataset, referred to as ZSGL, comprising the categories present in MSRC and Italian gesture datasets. Overall, our ZSGL dataset consisted of 26 categories, 65 discriminative attributes, and 16 attribute annotations and 400 examples per category. We used trainable recurrent networks and 3D CNNs to learn the spatio-temporal features. Next, we propose a simple yet effective
Indexed as
Identifiers
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.