arXiv (All)
open access
Concentration, Not Uncertainty: Why Targeted Synthetic Data Doesn't Help Camouflaged Object Detection
Camouflaged object detection requires pixel-accurate masks, but obtaining such annotations is slow and costly, making synthetic training images an attractive alternative. Under a fixed generation budget, however, it remains unclear which real-image regions to target for synthetic data generation. We
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
- Consensus-Aware Multi-Source Fusion for Reference-Guided Camouflaged Object Detection#1143348
- Camouflaged Object Detection#403987
- Refining Ground Truth Poses in Autonomous Driving Datasets via Neural Rendering#1035285
- Generative Verification: Rethinking the Uncertainty Signal for Active Learning of Object Detection#1034030
- DailyBench: A Unified Benchmark for AI-Generated and Manipulated Images from Modern Generative Models#821962
Record · ID 1234174
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