Abstract: The recent success in Computer Vision has been mostly attributed to improved results using deep learning models trained on large labeled datasets. Many of these datasets have been labeled by humans. The labeling process, however, can be time-consuming, and in many applications, it may require expertise that could be costly to acquire. In order

Abstract: Programming robots to manipulate articulated objects such as drawers, doors, or locks is a challenging task. One of the major reasons for this difficulty is that robots must physically interact with objects, and even minor errors during manipulation can result in significant internal forces that may cause damage. While robots struggle with these manipulation

Abstract: It has recently been proposed that a minimal condition of intelligence is the ability to form accurate predictions (Tjøstheim & Stephens 2021). In this talk, I evaluate the promise of this view for integrating intelligence research across subdisciplines within the cognitive and life sciences. I argue that this view combines two desirable features: (1)

Abstract: Event cameras are novel bio-inspired sensors that naturally respond to motion in the scene. They have promising advantages, namely a high dynamic range, little motion blur and low latency. But how can we leverage these advantages for vision tasks such as animal behavior quantification? In this talk I will  present two applications developed at

Abstract: Artificial deep neural networks (DNNs) are used in many different ways to address scientific questions about how biological vision works. In spite of the wide usage of DNNs in this context, their scientific value is periodically questioned. I will argue that DNNs are good in three ways for vision science: for prediction, for explanation,

More details to follow. This talk will take place in person at SCIoI.