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cs.CV2025
ReMatch: Boosting Representation through Matching for Multimodal Retrieval
Qianying Liu, Xiao Liang, Zhiqiang Zhang +6
We present ReMatch, a framework that leverages the generative strength of MLLMs for multimodal retrieval. Previous approaches treated an MLLM as a simple encoder, ignoring its gene…
cs.CV2025
AesTest: Measuring Aesthetic Intelligence from Perception to Production
Guolong Wang, Heng Huang, Zhiqiang Zhang +3
Perceiving and producing aesthetic judgments is a fundamental yet underexplored capability for multimodal large language models (MLLMs). However, existing benchmarks for image aest…
cs.CV2025
NOTA: Multimodal Music Notation Understanding for Visual Large Language Model
Mingni Tang, Jiajia Li, Lu Yang +5
Symbolic music is represented in two distinct forms: two-dimensional, visually intuitive score images, and one-dimensional, standardized text annotation sequences. While large lang…