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This work introduces SVGLM, a framework that uses scalable vector graphics (SVG) primitives to connect text and image in reasoning tasks, and exploits the duality of SVG as both image description and text instructions, yielding a more compact, interpretable solution to equip general VLMs with the capability of generati...
APT-Tune is proposed, a parameter-efficient recipe that teaches VLMs to use causal transitions without forgetting how to answer video questions, and substantially improves APT recall while also improving event-level video transfer.
VLAFP is the first deep audio fingerprinting model capable of processing audio of variable length, for both training and testing, and outperforms existing state-of-the-arts in live audio identification and audio retrieval across three real-world datasets.
Experimental results demonstrate that Phys4D substantially improves fine-grained spatiotemporal and physical consistency compared to appearance-driven baselines, while maintaining strong generative performance.
VIBE is introduced, a novel text-and-video-to-music (T+V2M) generation model that leverages a depth-wise cross-layer conditioning mechanism that dynamically bridges the planning and diffusion refinement heads and a comprehensive reward modeling taxonomy, optimizing for both hard, verifiable constraints and soft, subjec...
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