arXiv Machine Learning August 4, 2026 By Ege Ozguroglu, Junbang Liang, Ruoshi Liu, Mia Chiquier, Michael DeTienne, Wesley Wei Qian, Alexandra Horowitz, Andrew Owens, Carl Vondrick

New York Smells: A Large Multimodal Dataset for Olfaction

Read the original on arXiv Machine Learning →

arXiv:2511. 20544v2 Announce Type: replace-cross Abstract: While olfaction is central to how animals perceive the world, this rich chemical sensory modality remains largely inaccessible to machines.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

arXiv AI

Jul 8

What Images Cannot Say: Language-Guided Olfactory Representation Learning

arXiv:2607. 06402v1 Announce Type: cross Abstract: Images tell us what a scene looks like, but rarely what it would feel like to be there.

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arXiv AI

Jun 19

Mitigating Simplicity Bias in OOD Detection through Object Co-occurrence Analysis

arXiv:2605. 07821v2 Announce Type: replace-cross Abstract: Out-of-distribution (OOD) detection is crucial for ensuring the reliability of deep learning models.

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arXiv Machine Learning

Jun 30

Meta-learning as a principle for human-like visual representations

arXiv:2606. 28399v1 Announce Type: cross Abstract: The structure of human visual representations underpins our capacity for adaptive behaviour.

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arXiv Machine Learning

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Visual Species Recognition with Large Multimodal Models as Post-Hoc Correctors

arXiv:2512. 15748v2 Announce Type: replace Abstract: Visual Species Recognition (VSR) is a fundamental task in scientific disciplines that require species-level identification, including ecology, palynology, evolutionary biology, systematics, and phylogenetics.

By Tian Liu, Anwesha Basu, James Caverlee, Shu Kong

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arXiv Machine Learning

Jul 17

Multimodality as Supervision: Self-Supervised Specialization to the Test Environment via Multimodality

arXiv:2607. 14721v1 Announce Type: cross Abstract: Cross-modal learning, i.

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arXiv Machine Learning

Jun 2

UrbanFusion: Stochastic Multimodal Fusion for Contrastive Learning of Robust Spatial Representations

arXiv:2510. 13774v2 Announce Type: replace Abstract: Forecasting urban phenomena such as housing prices and public health indicators requires the effective integration of various geospatial data.

By Dominik J. M\"uhlematter, Lin Che, Ye Hong, Martin Raubal, Nina Wiedemann

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Related stories

What Images Cannot Say: Language-Guided Olfactory Representation Learning

Mitigating Simplicity Bias in OOD Detection through Object Co-occurrence Analysis

Meta-learning as a principle for human-like visual representations

Visual Species Recognition with Large Multimodal Models as Post-Hoc Correctors

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UrbanFusion: Stochastic Multimodal Fusion for Contrastive Learning of Robust Spatial Representations