Ultra-fast image categorization in vivo and in silico

@article{Jeremie2022UltrafastIC,
  title={Ultra-fast image categorization in vivo and in silico},
  author={Jean-Nicolas J'er'emie and Laurent Udo Perrinet},
  journal={ArXiv},
  year={2022},
  volume={abs/2205.03635}
}
— Humans are able to robustly categorize images and can, for instance, detect the presence of an animal in a briefly flashed image in as little as 120 ms . Initially inspired by neuroscience, deep-learning algorithms literally bloomed up in the last decade such that the accuracy of machines is at present superior to humans for visual recognition tasks. However, these artificial networks are usually trained and evaluated on very specific tasks, for instance on the 1000 separate categories of I MAGE… 

References

SHOWING 1-10 OF 50 REFERENCES

How transferable are features in deep neural networks?

This paper quantifies the generality versus specificity of neurons in each layer of a deep convolutional neural network and reports a few surprising results, including that initializing a network with transferred features from almost any number of layers can produce a boost to generalization that lingers even after fine-tuning to the target dataset.

Animal Detection in Natural Images: Effects of Color and Image Database

The ANID image database is better suited for the investigation of the processing of natural scenes than other databases commonly used, and is concluded that ultra-fast processing of animal images is possible irrespective of the particular database.

Very Deep Convolutional Networks for Large-Scale Image Recognition

This work investigates the effect of the convolutional network depth on its accuracy in the large-scale image recognition setting using an architecture with very small convolution filters, which shows that a significant improvement on the prior-art configurations can be achieved by pushing the depth to 16-19 weight layers.

Convolutional neural networks trained with a developmental sequence of blurry to clear images reveal core differences between face and object processing

The results suggest that blurry vision during infancy is insufficient to account for the robustness of adult vision to blurry objects, providing novel computational evidence showing how face recognition, unlike object recognition, allows for more holistic processing.

Brain-Score: Which Artificial Neural Network for Object Recognition is most Brain-Like?

The internal representations of early deep artificial neural networks were found to be remarkably similar to the internal neural representations measured experimentally in the primate brain, and a composite of multiple neural and behavioral benchmarks that score any ANN on how similar it is to the brain’s mechanisms for core object recognition is developed.

The Time-Course of Visual Categorizations: You Spot the Animal Faster than the Bird

This work compared human processing speed when categorizing natural scenes as containing either an animal (superordinate level), or a specific animal (bird or dog, basic level) and found the basic level category is accessed as fast as the superordinate category.

A feedforward architecture accounts for rapid categorization

It is shown that a specific implementation of a class of feedforward theories of object recognition (that extend the Hubel and Wiesel simple-to-complex cell hierarchy and account for many anatomical and physiological constraints) can predict the level and the pattern of performance achieved by humans on a rapid masked animal vs. non-animal categorization task.

Edge co-occurrences can account for rapid categorization of natural versus animal images

The statistics of edge co-occurrences alone are sufficient to perform a rough yet robust (translation, scale, and rotation invariant) scene categorization and suggest new algorithms for image classification and signal processing that exploit correlations between low-level structure and the underlying semantic category.

Key Visual Features for Rapid Categorization of Animals in Natural Scenes

The results support fast diagnostic recognition of animals based on key intermediate features and priming based on the subject's expertise as well as agreement with other experimental and modeling studies.

Comparison of deep neural networks to spatio-temporal cortical dynamics of human visual object recognition reveals hierarchical correspondence

It was shown that the DNN captured the stages of human visual processing in both time and space from early visual areas towards the dorsal and ventral streams and provided an algorithmically informed view on the spatio-temporal dynamics of visual object recognition in the human visual brain.