Introduces SmellNet-V synthetic visuo-olfactory dataset and See & Sniff self-supervised framework that learns aligned representations and produces smell saliency maps.
New york smells: A large multimodal dataset for olfaction
3 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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2026 3representative citing papers
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.
A simulation-to-real navigation policy enables a quadrotor to locate an odor source using only basic olfaction sensors and optional vision, validated in indoor real-world flights.
citing papers explorer
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See & Sniff: Learning Visuo-Olfactory Representations
Introduces SmellNet-V synthetic visuo-olfactory dataset and See & Sniff self-supervised framework that learns aligned representations and produces smell saliency maps.
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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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Chasing Ghosts: A Simulation-to-Real Olfactory Navigation Stack with Optional Vision Augmentation
A simulation-to-real navigation policy enables a quadrotor to locate an odor source using only basic olfaction sensors and optional vision, validated in indoor real-world flights.