AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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VehDyn provides a systematic foundation for developing driving world models that are physically consistent and visually realistic, and benchmarks 12 state-of-the-art video world models.
To address the inherent limitations of Vision-Language Models in long-tail object retrieval for autonomous driving, this paper proposes a Dual-Granularity Structured Scene Retrieval (DG-SSR) architecture. By decoupling text queries and visual features, we introduce a parameter-free mechanism that fuses local semantic s...
This work designs a plug-and-play and non-invasive spatio-vision module that elicits the spatial knowledge inherent in VLMs and innovatively leverage pseudo depth and camera information as supervision to guide the model in learning physically coherent representations.
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