arXiv (All)
open access
Exploiting answer-invariant redundancies in satellite imagery for efficient VLM inference on edge
Onboard vision-language models could enable satellites to answer queries directly, but exhaustive tiled inference over high-resolution imagery is slow and energy-intensive. We identify answer-invariant token redundancy (AITR): image tiles and vision tokens that can be removed without changing the fi
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
- WeatherReasonSeg: A Benchmark for Weather-Aware Reasoning Segmentation in Visual Language Models#820392
- Multi-level fusion for visual question answering on remote sensing images#1065947
- Informative Viewpoint Selection for Episodic-Memory Embodied Question Answering using Omnidirectional Images#1118000
- Selective Tool Use for Agentic Change Visual Question Answering in Remote Sensing#1084469
- Part Grounding, Not Action Knowledge: Locating the Bottleneck in VLM Affordance Prediction#1071032
Record · ID 1076958
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