SCENT uses VLM-generated scene descriptions as a semantic bridge to align electronic-nose signals with visual and textual embeddings, improving cross-modal smell retrieval and enabling object-context odor disentanglement.
In: CVPR (2017)
2 Pith papers cite this work. Polarity classification is still indexing.
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cs.CV 2years
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Splash partitions MLLM parameters into dormant and critical subspaces via significance quantification, updating only the dormant subspace for tactile alignment while preserving general capabilities and achieving SOTA on visuo-tactile benchmarks.
citing papers explorer
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What Images Cannot Say: Language-Guided Olfactory Representation Learning
SCENT uses VLM-generated scene descriptions as a semantic bridge to align electronic-nose signals with visual and textual embeddings, improving cross-modal smell retrieval and enabling object-context odor disentanglement.
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Wake up for Touch! Mask-isolated Tactile Alignment Learning in MLLMs
Splash partitions MLLM parameters into dormant and critical subspaces via significance quantification, updating only the dormant subspace for tactile alignment while preserving general capabilities and achieving SOTA on visuo-tactile benchmarks.