5 papers
Learning to See the Elephant in the Room: Self-Supervised Context Reasoning in Humans and AI
Xiao Liu, Soumick Sarker, Ankur Sikarwar +4
Humans rarely perceive objects in isolation but interpret scenes through relationships among co-occurring elements. How such contextual knowledge is acquired without explicit super…
Stretching Beyond the Obvious: A Gradient-Free Framework to Unveil the Hidden Landscape of Visual Invariance
Lorenzo Tausani, Paolo Muratore, Morgan B. Talbot +3
Uncovering which feature combinations are encoded by visual units is critical to understanding how images are transformed into representations that support recognition. While exist…
HumorDB: Can AI understand graphical humor?
Vedaant Jain, Felipe dos Santos Alves Feitosa, Gabriel Kreiman
Despite significant advancements in image segmentation and object detection, understanding complex scenes remains a significant challenge. Here, we focus on graphical humor as a pa…
Can Machines Imitate Humans? Integrative Turing-like tests for Language and Vision Demonstrate a Narrowing Gap
Mengmi Zhang, Elisa Pavarino, Xiao Liu +20
As AI becomes increasingly embedded in daily life, ascertaining whether an agent is human is critical. We systematically benchmark AI's ability to imitate humans in three language…
L-WISE: Boosting Human Visual Category Learning Through Model-Based Image Selection and Enhancement
Morgan B. Talbot, Gabriel Kreiman, James J. DiCarlo +1
The currently leading artificial neural network models of the visual ventral stream - which are derived from a combination of performance optimization and robustification methods -…