AIMC Topic: Visual Perception

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How well do rudimentary plasticity rules predict adult visual object learning?

PLoS computational biology
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...

Thinking beyond the ventral stream: Comment on Bowers et al.

The Behavioral and brain sciences
Bowers et al. rightly emphasise that deep learning models often fail to capture constraints on visual perception that have been discovered by previous research. However, the solution is not to discard deep learning altogether, but to design stimuli a...

A deep new look at color.

The Behavioral and brain sciences
Bowers et al. counter deep neural networks (DNNs) as good models of human visual perception. From our color perspective we feel their view is based on three misconceptions: A misrepresentation of the state-of-the-art of color perception; the type of ...

Neural networks need real-world behavior.

The Behavioral and brain sciences
Bowers et al. propose to use controlled behavioral experiments when evaluating deep neural networks as models of biological vision. We agree with the sentiment and draw parallels to the notion that "neuroscience needs behavior." As a promising path f...

Modelling human vision needs to account for subjective experience.

The Behavioral and brain sciences
Vision is inseparably connected to perceptual awareness which can be seen as the culmination of sensory processing. Studies on conscious vision reveal that object recognition is just one of the means through which our representation of the world is b...

Hierarchies in Visual Pathway: Functions and Inspired Artificial Vision.

Advanced materials (Deerfield Beach, Fla.)
The development of artificial intelligence has posed a challenge to machine vision based on conventional complementary metal-oxide semiconductor (CMOS) circuits owing to its high latency and inefficient power consumption originating from the data shu...

A novel feature-scrambling approach reveals the capacity of convolutional neural networks to learn spatial relations.

Neural networks : the official journal of the International Neural Network Society
Convolutional neural networks (CNNs) are one of the most successful computer vision systems to solve object recognition. Furthermore, CNNs have major applications in understanding the nature of visual representations in the human brain. Yet it remain...

Species-specific wiring of cortical circuits for small-world networks in the primary visual cortex.

PLoS computational biology
Long-range horizontal connections (LRCs) are conspicuous anatomical structures in the primary visual cortex (V1) of mammals, yet their detailed functions in relation to visual processing are not fully understood. Here, we show that LRCs are key compo...

Enhancing Human-Robot Collaboration through a Multi-Module Interaction Framework with Sensor Fusion: Object Recognition, Verbal Communication, User of Interest Detection, Gesture and Gaze Recognition.

Sensors (Basel, Switzerland)
With the increasing presence of robots in our daily lives, it is crucial to design interaction interfaces that are natural, easy to use and meaningful for robotic tasks. This is important not only to enhance the user experience but also to increase t...

Towards Recognition of Human Actions in Collaborative Tasks with Robots: Extending Action Recognition with Tool Recognition Methods.

Sensors (Basel, Switzerland)
This paper presents a novel method for online tool recognition in manual assembly processes. The goal was to develop and implement a method that can be integrated with existing Human Action Recognition (HAR) methods in collaborative tasks. We examine...