Convolutional Neural Networks for Object Recognition

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Questions and Answers

What is the role of the visual ventral pathway in primates?

Involved in object recognition (correct)

Involved in image transformation

Involved in object categorization

Involved in background recognition

Where does the ventral visual pathway extend from in primates?

From V1 to the IT cortex (correct)

From the temporal lobe to V1

From IT cortex to the temporal lobe

From V1 to the temporal lobe

What technique did Hung et al. use to show the activity of IT neurons?

Deep convolutional neural networks

Object recognition tasks

Classifier-based readout technique (correct)

Image transformation

What type of information is contained in the activity of small populations of IT neurons?

Object category and identity Signup and view all the answers

What is the characteristic of core object recognition?

Ability to rapidly identify objects in a single natural fixation Signup and view all the answers

What is the correlation between monkey performance and human performance confusion patterns?

0.78 Signup and view all the answers

What is the role of deep convolutional neural networks (DCNNs) in object recognition?

To model the ventral visual pathway Signup and view all the answers

What level of performance have DCNNs achieved on object categorization tasks?

Near-human-level performance Signup and view all the answers

What is the average difference in time for the IT decode solutions for challenge images compared to control images?

30 ms Signup and view all the answers

What was the hypothesis about purely feedforward DCNNs in relation to IT neural responses?

They should accurately predict IT neural responses for control images but fail to predict for challenge images Signup and view all the answers

What was the purpose of the partial least square analysis?

To estimate the set of weights and biases that allows to best predict RTRAIN from FTRAIN Signup and view all the answers

What was the number of images used for data collection?

1320 Signup and view all the answers

What was used to generate predictions for the test image evoked activations of the model FTEST?

The best set of weights and biases Signup and view all the answers

What is the percentage of the explainable IT neural response variance predicted by the fc7 layer of AlexNet during the early response phase?

44.3 ± 0.7% Signup and view all the answers

What was the time bin size used for collecting neural responses?

10 ms Signup and view all the answers

What was the time range during which the fc7 layer of AlexNet predicted 44.3 ± 0.7% of the explainable IT neural response variance?

90-110 ms Signup and view all the answers

What was mapped for the train images?

The image evoked activations of the DCNN model Signup and view all the answers

What was the purpose of using the DCNN features?

To predict the time-evolving IT population response Signup and view all the answers

What is the name of the model used to predict the IT neural response variance?

DCNN Signup and view all the answers

What is the term used to describe the ability of the DCNN to predict the IT population pattern?

IT predictivity Signup and view all the answers

What was the method used to estimate the set of weights and biases?

Partial least square analysis Signup and view all the answers

What happens to the ability of the DCNN to predict the IT population pattern over time?

It worsens significantly Signup and view all the answers

What is the commonality between the neural shape representation of rhesus monkeys and humans?

Supports object recognition Signup and view all the answers

What type of neural networks display internal feature representations similar to neuronal representations in the primate ventral visual stream?

Deep Convolutional Neural Networks (DCNNs) Signup and view all the answers

What is the limitation of using low-resolution behavioral measures to evaluate DCNN models?

They may not capture certain failures of the models Signup and view all the answers

How many binary object discrimination tasks were used in the study by Rajalingham et al.?

276 Signup and view all the answers

What was the purpose of randomly changing viewing parameters in the images used in the study?

To enforce invariant object recognition behavior Signup and view all the answers

How long did the monkeys hold fixation on a central point before the test image appeared?

200 ms Signup and view all the answers

What was shown to the monkeys immediately after the extinction of the test image?

Two choice images Signup and view all the answers

What was the visual angle of the test image shown to the monkeys?

6° of visual angle Signup and view all the answers

What was the primary task used to compare the behavioral performance of primates and current DCNNs?

Binary object discrimination task Signup and view all the answers

How many images were used in the object discrimination task?

1,320 images Signup and view all the answers

What was the difference in reaction time for challenge images compared to control images in humans?

25 ms Signup and view all the answers

What is the term used to describe the time at which object identities are formed in the IT cortex?

Object solution time Signup and view all the answers

How often was the neural decode accuracy (NDA) estimated for each image?

Every 10 ms Signup and view all the answers

What is the purpose of training and testing linear classifiers per object independently at each time bin?

To estimate the neural decode accuracy Signup and view all the answers

What is the significance of the time point at which the NDA measured for each image reaches the level of the behavioral accuracy of each subject?

It indicates the object solution time Signup and view all the answers

What was observed in terms of the accuracy of the IT decodes for both control and challenge images?

The accuracy of the IT decodes became equal to the behavioral accuracy of the monkeys at some time point Signup and view all the answers

Study Notes

Convolutional Neural Networks and Object Recognition

In primates, the visual ventral pathway is critically involved in object recognition, extending from V1 to the IT cortex in the temporal lobe.
The ventral visual pathway gradually "untangles" information about object identity.
Classifier-based readout techniques have shown that the activity of small populations of IT neurons (~300 units) contains accurate and robust information about object identity and category over short time intervals (as small as 12.5 milliseconds).

Deep Convolutional Neural Networks (DCNNs)

DCNNs are good candidates for models of the ventral visual pathway and have achieved near-human-level performance on challenging object categorization tasks.
DCNNs, optimized by supervised training on large-scale category-labeled image sets, display internal feature representations similar to neuronal representations along the primate ventral visual stream.

Comparison of DCNNs with Human and Monkey Performance

Monkey performance shows a pattern of object confusion that is highly correlated with human performance confusion pattern (0.78), suggesting a common neural shape representation that directly supports object recognition.
However, several studies have shown that DCNN models can diverge drastically from humans in object recognition behavior.
A recent study employed both low- and high-resolution measurements of behavior (over a million behavioral trials) from 1472 anonymous humans and five male macaque monkeys with 2400 images for over 276 binary object discrimination tasks.

Object Recognition Tasks

Each trial consisted of a central point fixation, followed by a test image (6° of visual angle) appearing at the center for 100 ms, and then two choice images, each displaying the canonical view of a single object with no background.
The behavioral performance of primates (humans and macaques) and current DCNNs was compared using a binary object discrimination task, with 1,320 images (132 images per object) in which the object belonged to 1 of 10 different categories.

Reaction Times and Object Solution Time

Macaques and humans outperform AlexNet (2012), with 266 challenge images and 149 control images.
Reaction times (RTs) for both humans and macaques for challenge images were significantly higher than for the control images (monkeys: ΔRT = 11.9 ms, humans: ΔRT = 25 ms), suggesting that additional processing time is required for the challenge images.
The object solution time (OST) refers to the time at which the NDA measured for each image reaches the level of the behavioral accuracy of each subject (pooled monkey).

IT Predictivity Across Time from Feedforward DCNNs

IT predictivity from feedforward DCNNs was investigated using a partial least square analysis to predict the time-evolving IT population response.
The data collection included neural responses for each of the 1320 images (50 repetitions) across 10 ms time bins.
The IT predictivity of the model was computed by comparing the predictions from the synthetic neuron with the test image evoked neural features.
The fc7 layer of AlexNet predicted 44.3 ± 0.7% of the explainable IT neural response variance during the early (putative largely feedforward) response phase (90–110 ms).

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Description

This quiz explores the role of convolutional neural networks in object recognition, comparing them to the human brain's visual ventral pathway. It covers the involvement of V1 to IT cortex in primates.