This valuable study introduces a self-supervised machine learning method to classify C. elegans postures and behaviors directly from video data, offering an alternative to the skeleton-based ...

Infants as young as 15 months can use language context to infer the meaning of words referring to objects they’ve never seen.

A GMAT expert explains why visual learning is the ultimate study hack—and how a new OnDemand course helps high achievers ...

A new study questions the longstanding view that the visual system is divided into two pathways, one for object-recognition and the other for spatial tasks. Using computational vision models, ...

Abstract: Self-supervised contrastive learning methods offer a promising approach to the small-sample synthetic aperture radar (SAR) automatic target recognition (ATR) problem by autonomously ...

This important study reports a reanalysis of one experiment of a previously-published report to characterize the dynamics of neural population codes during visual working memory in the presence of ...

This research introduces MMSearch-R1, which represents a pioneering approach to equip LMMs with active image search capabilities through an end-to-end reinforcement learning framework. This robust ...

method to learn the visual representation directly on the multi-instance datasets (e.g., PASCAL VOC and COCO) for dense prediction tasks (e.g., object detection and instance segmentation). We present ...

"The paper offers a new perspective on understanding the formation of representations in neural networks through the CRH and PAH," says Poggio. "This provides a framework for unifying existing ...