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cs.CV2026

OpenEarthAgent: A Unified Framework for Tool-Augmented Geospatial Agents

Akashah Shabbir, Muhammad Umer Sheikh, Muhammad Akhtar Munir +8

Recent progress in multimodal reasoning has enabled agents that interpret imagery, connect it with language, and execute structured analytical tasks. Extending these capabilities t…

cs.CV2026

Objects Before Words: Object-First Inductive Biases for Grounding Language in Child-View Video

Sathira Silva, Abrham Kahsay Gebreselasie, Muhammad Umer Sheikh +3

Learning grounded word meaning from natural experience requires resolving two ambiguities in infant-view recordings: when the named referent appears and where it is in a cluttered…

cs.CV2026

Agentic AI for Remote Sensing: Technical Challenges and Research Directions

Muhammad Akhtar Munir, Muhammad Umer Sheikh, Akashah Shabbir +5

Earth Observation (EO) is moving beyond static prediction toward multi-step analytical workflows that require coordinated reasoning over data, tools, and geospatial state. While fo…

cs.CV2026

ThinkGeo: Evaluating Tool-Augmented Agents for Remote Sensing Tasks

Akashah Shabbir, Muhammad Akhtar Munir, Akshay Dudhane +6

Recent progress in large language models (LLMs) has enabled tool-augmented agents capable of solving complex real-world tasks through step-by-step reasoning. However, existing eval…

cs.CV2025

Towards PerSense++: Advancing Training-Free Personalized Instance Segmentation in Dense Images

Muhammad Ibraheem Siddiqui, Muhammad Umer Sheikh, Hassan Abid +2

Segmentation in dense visual scenes poses significant challenges due to occlusions, background clutter, and scale variations. To address this, we introduce PerSense, an end-to-end,…

cs.CV2025

PerSense: Training-Free Personalized Instance Segmentation in Dense Images

Muhammad Ibraheem Siddiqui, Muhammad Umer Sheikh, Hassan Abid +1

The emergence of foundational models has significantly advanced segmentation approaches. However, challenges still remain in dense scenarios, where occlusions, scale variations, an…