NCBI PubMed Central
open access
IGNN: An improved graph neufral network with integrated attention and pre-message-passing for few-shot image classification.
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Hierarchical Response Preservation for Continual Adaptation of Zero-Shot Graph-Text Models#1118319
- Graph Learning with Spectral Connectivity Priors for Scarce Data#1063489
- Construction of a fine-grained retrieval model for archival text-image based on the integration of scene graph generation and attention mechanism.#402682
- High-Dimensional Learning Dynamics of Attention-Indexed Models#639684
- A Universal Knowledge Embedded Contrastive Learning Framework for Hyperspectral Image Classification#1123901
Record · ID 143038
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive