3 citations · 5 across the 5 of their papers we have counts for
5 papers
Tracking objects that change in appearance with phase synchrony
Sabine Muzellec, Drew Linsley, Alekh K. Ashok +4
Objects we encounter often change appearance as we interact with them. Changes in illumination (shadows), object pose, or the movement of non-rigid objects can drastically alter av…
Extreme Image Transformations Facilitate Robust Latent Object Representations
Girik Malik, Dakarai Crowder, Ennio Mingolla
Adversarial attacks can affect the object recognition capabilities of machines in wild. These can often result from spurious correlations between input and class labels, and are pr…
Extreme Image Transformations Affect Humans and Machines Differently
Girik Malik, Dakarai Crowder, Ennio Mingolla
Some recent artificial neural networks (ANNs) claim to model aspects of primate neural and human performance data. Their success in object recognition is, however, dependent on exp…
The Challenge of Appearance-Free Object Tracking with Feedforward Neural Networks
Girik Malik, Drew Linsley, Thomas Serre +1
Nearly all models for object tracking with artificial neural networks depend on appearance features extracted from a "backbone" architecture, designed for object recognition. Indee…
Tracking Without Re-recognition in Humans and Machines
Drew Linsley, Girik Malik, Junkyung Kim +3
Imagine trying to track one particular fruitfly in a swarm of hundreds. Higher biological visual systems have evolved to track moving objects by relying on both appearance and moti…