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This paper proposes PACE, a two-stage framework that progressively expands both the representation and trainable parameter spaces, and introduces Focal Embedding Loss, a confidence-adaptive objective that downweights queries with high positive retrieval confidence while emphasizing ambiguous queries with competitive ne...
This work builds a synthetic data engine that leverages a spatio-temporal scene graph as a difficulty measure and casts difficulty-controlled query synthesis as a constraint programming problem, producing difficulty-graded data for both evaluation and training and proposes CurrSTVG, a curriculum reinforcement learning...
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