✨Artemis: Structured Visual Reasoning for Perception Policy Learning

📝 Summary:
Artemis improves visual perception by using structured spatial reasoning with label bounding-box pairs instead of linguistic intermediate reasoning. This avoids language ambiguity, enables direct supervision, and leads to strong performance and generalization across diverse visual tasks.

🔹 Publication Date: Published on Dec 1

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.01988
• PDF: https://arxiv.org/pdf/2512.01988
• Project Page: https://vi-ocean.github.io/projects/artemis/
• Github: https://github.com/WayneTomas/Artemis

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#VisualPerception #ComputerVision #SpatialReasoning #AI #MachineLearning

✨VisionFoundry: Teaching VLMs Visual Perception with Synthetic Images

📝 Summary:
VisionFoundry creates synthetic visual question answering data using LLMs and text-to-image models to improve VLM visual perception. Training with this targeted data significantly boosts model performance on visual perception benchmarks like MMVP and CV-Bench-3D.

🔹 Publication Date: Published on Apr 10

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.09531
• PDF: https://arxiv.org/pdf/2604.09531
• Project Page: https://zlab-princeton.github.io/VisionFoundry/

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For more data science resources:
✓ https://xn--r1a.website/DataScienceT

#VLM #VisualPerception #SyntheticData #LLM #AI