20 citations · 62 across the 19 of their papers we have counts for
8 papers · 1 filter
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…
Red Teaming with Mind Reading: White-Box Adversarial Policies Against RL Agents
Stephen Casper, Taylor Killian, Gabriel Kreiman +1
Adversarial examples can be useful for identifying vulnerabilities in AI systems before they are deployed. In reinforcement learning (RL), adversarial policies can be developed by…
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…
Error-driven Input Modulation: Solving the Credit Assignment Problem without a Backward Pass
Giorgia Dellaferrera, Gabriel Kreiman
Supervised learning in artificial neural networks typically relies on backpropagation, where the weights are updated based on the error-function gradients and sequentially propagat…