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From the 1 of 6 linked papers with an AI index.

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6 papers

cs.CV2026

MVFusion-GS: Motion-Variance Guided Temporal Attention for High-Quality Dynamic Gaussian Splatting

Jianwei Hu, Tingxuan Huang, Hengyu Zhou +4

The paper introduces MVFusion-GS, which adds motion‑variance guided refinement and a transformer‑based temporal attention module to deformation networks for dynamic 3D Gaussian spl…

cs.CV2026

AirGroundBench: Probing Spatial Intelligence in Multimodal Large Models under Heterogeneous Multi-View Embodied Collaboration

Haotian Li, Yida Wang, Leyuan Wang +7

In recent years, multimodal large language models (MLLMs) have shown strong potential for embodied intelligence, yet their ability to maintain geometrically consistent spatial unde…

cs.RO2026

Intelligent Automation for Embodied Benchmark Construction: Pipelines, Embodiments, Simulators, and Trends

Jinshan Lai, Jianwei Hu, Baoyang Jiang +7

Embodied intelligence now spans navigation, household assistance, manipulation, autonomous driving, aerial agents, and multimodal large-model control. This expansion has made bench…

cs.AI2026

Embodied-BenchClaw: An Autonomous Multi-Agent System for Embodied Spatial Intelligence Benchmark Construction

Baoyang Jiang, Fengchun Zhang, Leyuan Wang +7

Benchmarks are essential for evaluating embodied spatial intelligence, yet their construction is labor-intensive, hard to reuse, and difficult to maintain. Existing embodied benchm…

cs.CV2026

CO-EVO: Co-evolving Semantic Anchoring and Style Diversification for Federated DG-ReID

Fengchun Zhang, Qiang Ma, Liuyu Xiang +3

Federated domain generalization for person re-identification (FedDG-ReID) aims to collaboratively train a pedestrian retrieval model across multiple decentralized source domains su…

cs.LG2025

FedSSG: Expectation-Gated and History-Aware Drift Alignment for Federated Learning

Zhanting Zhou, Jinshan Lai, Fengchun Zhang +2

Non-IID data and partial participation induce client drift and inconsistent local optima in federated learning, causing unstable convergence and accuracy loss. We present FedSSG, a…