17 citations · 35 across the 6 of their papers we have counts for
13 papers · 1 filter
Categorizing the Visual Environment and Analyzing the Visual Attention of Dogs
Shreyas Sundara Raman, Madeline H. Pelgrim, Daphna Buchsbaum +1
Dogs have a unique evolutionary relationship with humans and serve many important roles e.g. search and rescue, blind assistance, emotional support. However, few datasets exist to…
Harmonizing the object recognition strategies of deep neural networks with humans
Thomas Fel, Ivan Felipe, Drew Linsley +1
The many successes of deep neural networks (DNNs) over the past decade have largely been driven by computational scale rather than insights from biological intelligence. Here, we e…
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…
Recurrent neural circuits for contour detection
Drew Linsley, Junkyung Kim, Alekh Ashok +1
We introduce a deep recurrent neural network architecture that approximates visual cortical circuits. We show that this architecture, which we refer to as the gamma-net, learns to…
Stable and expressive recurrent vision models
Drew Linsley, Alekh Karkada Ashok, Lakshmi Narasimhan Govindarajan +2
Primate vision depends on recurrent processing for reliable perception. A growing body of literature also suggests that recurrent connections improve the learning efficiency and ge…