arXiv (All)
open access
REALM: An RGB- and Event-Aligned Latent Manifold for Cross-Modal Perception
Event cameras provide several unique advantages over standard frame-based sensors, including high temporal resolution, low latency, and robustness to extreme lighting. However, existing learning-based approaches for event processing are typically confined to narrow, task-specific silos and lack the
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
- EventGeM: Global-to-Local Feature Matching for Event-Based Visual Place Recognition#1035418
- EventCrab: Harnessing Frame and Point Synergy for Event-based Action Recognition and Beyond#149202
- EVIS: Real-Time Event Camera Simulation with Multimodal Supervision in NVIDIA Isaac Sim#1069858
- Hyper-RED: Scalable Event Pre-training via Semantic Hypergraph Distillation#1122809
- Language-Augmented Video Action Anticipation: Design Fundamentals, Benchmarks, and Open Challenges#1115621
Record · ID 1035478
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