arXiv (All)
open access
Depth-guided Geometric Adaptation for Few-shot Image Classification
Few-shot image classification (FSIC) frequently suffers from visual ambiguity, where background clutter and RGB appearance similarities hinder discriminative feature matching in data-scarce settings. To mitigate this, we propose DAFIC, a geometry-guided framework that incorporates monocular depth in
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
- JMT-YOLO: Multi-task Domain Adaptation via Segmentation-Guided Consistency Learning#1297253
- Transfer Learning and Domain Adaptation in Telecommunications#271050
- An exploration of domain generalisation through vision benchmarking, masking, and pruning#300477
- Improving Category-Aware Active Domain Adaptation via Uncertainty-Guided Region Coverage#1354487
- GlassFormer: Learning Real-time Glass Segmentation using Radar-Depth Fusion#1132045
Record · ID 1413097
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