arXiv Machine Learning June 30, 2026 By Can Demircan, Marcel Binz, Alireza Modirshanechi, Eric Schulz

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

Read the original on arXiv Machine Learning →

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

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

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arXiv Machine Learning

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arXiv:2503. 13212v3 Announce Type: replace Abstract: Alignment between human brain networks and artificial models has become an active research area in vision science and machine learning.

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Semantic Generative Tuning for Unified Multimodal Models

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EvoPrompt: Guided Prompt Evolution for Vision-Language Models Adaptation

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Human-like Object Grouping in Self-supervised Vision Transformers

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Image Generators are Generalist Vision Learners

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arXiv Machine Learning

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arXiv:2608. 05424v1 Announce Type: cross Abstract: Deep vision models exploit shortcuts, relying on cues that correlate with supervision signals.

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