AIMC Topic: Pattern Recognition, Visual
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Bio-inspired deep neural local acuity and focus learning for visual image recognition.
Neural networks : the official journal of the International Neural Network Society
Sep 17, 2024
In the field of computer vision and image recognition, enabling the computer to discern target features while filtering out irrelevant ones poses a challenge. Drawing insights from studies in biological vision, we find that there is a local visual ac...
Cracking the neural code for word recognition in convolutional neural networks.
PLoS computational biology
Sep 6, 2024
Learning to read places a strong challenge on the visual system. Years of expertise lead to a remarkable capacity to separate similar letters and encode their relative positions, thus distinguishing words such as FORM and FROM, invariantly over a lar...
The Quest for an Integrated Set of Neural Mechanisms Underlying Object Recognition in Primates.
Annual review of vision science
Sep 2, 2024
Inferences made about objects via vision, such as rapid and accurate categorization, are core to primate cognition despite the algorithmic challenge posed by varying viewpoints and scenes. Until recently, the brain mechanisms that support these capab...
Effects of machine learning errors on human decision-making: manipulations of model accuracy, error types, and error importance.
Cognitive research: principles and implications
Aug 26, 2024
This study addressed the cognitive impacts of providing correct and incorrect machine learning (ML) outputs in support of an object detection task. The study consisted of five experiments that manipulated the accuracy and importance of mock ML output...
Manipulating and measuring variation in deep neural network (DNN) representations of objects.
Cognition
Aug 19, 2024
We explore how DNNs can be used to develop a computational understanding of individual differences in high-level visual cognition given their ability to generate rich meaningful object representations informed by their architecture, experience, and t...
The attentive reconstruction of objects facilitates robust object recognition.
PLoS computational biology
Jun 13, 2024
Humans are extremely robust in our ability to perceive and recognize objects-we see faces in tea stains and can recognize friends on dark streets. Yet, neurocomputational models of primate object recognition have focused on the initial feed-forward p...
A robust event-driven approach to always-on object recognition.
Neural networks : the official journal of the International Neural Network Society
Jun 3, 2024
We propose a neuromimetic architecture capable of always-on pattern recognition, i.e. at any time during processing. To achieve this, we have extended an existing event-based algorithm (Lagorce et al., 2017), which introduced novel spatio-temporal fe...
Using drawings and deep neural networks to characterize the building blocks of human visual similarity.
Memory & cognition
May 30, 2024
Early in life and without special training, human beings discern resemblance between abstract visual stimuli, such as drawings, and the real-world objects they represent. We used this capacity for visual abstraction as a tool for evaluating deep neur...
A neurocomputational model of decision and confidence in object recognition task.
Neural networks : the official journal of the International Neural Network Society
Apr 12, 2024
How does the brain process natural visual stimuli to make a decision? Imagine driving through fog. An object looms ahead. What do you do? This decision requires not only identifying the object but also choosing an action based on your decision confid...
How well do rudimentary plasticity rules predict adult visual object learning?
PLoS computational biology
Dec 11, 2023
A core problem in visual object learning is using a finite number of images of a new object to accurately identify that object in future, novel images. One longstanding, conceptual hypothesis asserts that this core problem is solved by adult brains t...
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