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.
New York Smells: A Large Multimodal Dataset for Olfaction
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.
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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.
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.
arXiv Machine Learning
Jul 15
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.
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.
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.
arXiv Machine Learning
Jul 21
MultiLoReFT: Decoupling Shared and Modality-Specific Subspaces in Multimodal Learning via Low-Rank Representation Fine-Tuning
arXiv:2607. 16789v1 Announce Type: new Abstract: Real-world perception and decision making are inherently multimodal, integrating complementary signals across modalities.
arXiv Machine Learning
Jun 5
In-Context Multiple Instance Learning
arXiv:2606. 06458v1 Announce Type: new Abstract: Multiple Instance Learning (MIL) addresses problems where supervision is available at the level of bags of instances and has been successfully applied in fields ranging from computational pathology to satellite imagery.
arXiv Machine Learning
Jun 9
Neural Field Tokenizations with Hierarchy and Spatial Locality Priors
arXiv:2606. 08204v1 Announce Type: new Abstract: Neural fields parameterize data as functions from coordinates to values, providing a unified framework for representation learning across modalities.
arXiv AI
Jun 9
A Mixed Diet Makes DINO An Omnivorous Vision Encoder
arXiv:2602. 24181v2 Announce Type: replace-cross Abstract: Pre-trained vision encoders like DINOv2 have demonstrated exceptional performance on unimodal tasks.
arXiv AI
Aug 3
Leveraging Image Generators to Address Data Scarcity: The Gen4Regen Dataset for Forest Regeneration Mapping
arXiv:2605. 05627v2 Announce Type: replace-cross Abstract: Sustainable forest management relies on precise species composition mapping, yet traditional ground surveys are labour-intensive and geographically constrained.
arXiv Machine Learning
Jun 29
A Comparison of Fusion Techniques for Multi-Modal Human Activity Recognition on the HARMES Dataset
arXiv:2606. 27886v1 Announce Type: new Abstract: Recent advances in Human Activity Recognition (HAR) from wearable sensors have shown that multi-modal deep learning models consistently outperform their uni-modal counterparts.