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KAIST
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Autoregressive coordinate generation fundamentally breaks down on dense satellite imagery, but framing change localization as LLM selection over discrete candidate region tokens solves this bottleneck.
View-invariant video representations can be achieved without sacrificing the richness of view-variant semantics, leading to unprecedented performance in cross-view tasks.
