20 citations · 62 across the 19 of their papers we have counts for
13 papers · 1 filter
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…
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 -…
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…
Efficient Zero-shot Visual Search via Target and Context-aware Transformer
Zhiwei Ding, Xuezhe Ren, Erwan David +3
Visual search is a ubiquitous challenge in natural vision, including daily tasks such as finding a friend in a crowd or searching for a car in a parking lot. Human rely heavily on…
Learning to Learn: How to Continuously Teach Humans and Machines
Parantak Singh, You Li, Ankur Sikarwar +7
Curriculum design is a fundamental component of education. For example, when we learn mathematics at school, we build upon our knowledge of addition to learn multiplication. These…
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…
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…
Improving generalization by mimicking the human visual diet
Spandan Madan, You Li, Mengmi Zhang +2
We present a new perspective on bridging the generalization gap between biological and computer vision -- mimicking the human visual diet. While computer vision models rely on inte…
On the Efficacy of Co-Attention Transformer Layers in Visual Question Answering
Ankur Sikarwar, Gabriel Kreiman
In recent years, multi-modal transformers have shown significant progress in Vision-Language tasks, such as Visual Question Answering (VQA), outperforming previous architectures by…
Visual Search Asymmetry: Deep Nets and Humans Share Similar Inherent Biases
Shashi Kant Gupta, Mengmi Zhang, Chia-Chien Wu +2
Visual search is a ubiquitous and often challenging daily task, exemplified by looking for the car keys at home or a friend in a crowd. An intriguing property of some classical sea…
When Pigs Fly: Contextual Reasoning in Synthetic and Natural Scenes
Philipp Bomatter, Mengmi Zhang, Dimitar Karev +3
Context is of fundamental importance to both human and machine vision; e.g., an object in the air is more likely to be an airplane than a pig. The rich notion of context incorporat…
Tuned Compositional Feature Replays for Efficient Stream Learning
Morgan B. Talbot, Rushikesh Zawar, Rohil Badkundri +2
Our brains extract durable, generalizable knowledge from transient experiences of the world. Artificial neural networks come nowhere close to this ability. When tasked with learnin…
Look Twice: A Generalist Computational Model Predicts Return Fixations across Tasks and Species
Mengmi Zhang, Marcelo Armendariz, Will Xiao +5
Primates constantly explore their surroundings via saccadic eye movements that bring different parts of an image into high resolution. In addition to exploring new regions in the v…