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Role-aware matching in image retrieval can boost performance by up to 23% without the need for task-specific training.
AdaptiveEmbed reveals that tailoring representation capacity to individual sample needs can dramatically enhance multimodal retrieval performance.
KDPH's innovative use of Kent distributions allows it to absorb gradient conflicts, leading to superior performance in cross-modal hashing tasks.
Hallucinations in LVLMs can be cut by over 43% without sacrificing grounded object coverage, thanks to a novel verifier-guided approach.
LVLMs are better at spotting their own mistakes than generating correct answers in the first place, and this self-awareness can be exploited to reduce hallucinations.
