Article

Feature blindness: a challenge for understanding and modelling visual object recognition

2021-10-20

Abstract excerpt

Humans rely heavily on the shape of objects to recognise them. Recently, it has been argued that Convolutional Neural Networks (CNNs) can also show a shape-bias, provided their learning environment contains this bias. This has led to the proposal that CNNs provide good mechanistic models of shape-bias and, more generally, human visual processing. However, it is also possible that humans and CNNs show a shape-bias...

Topics

Open a Topic to create a Post that cites this publication.

Identifiers and source

Literature Corpus work: 072529f1-79a7-5089-be6b-9c5cafb3b67a
DOI: 10.1101/2021.10.20.465074

Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest

Feature blindness: a challenge for understanding and modelling visual object recognitionDOI 10.1101/2021.10.20.465074Read

Teaching deep networks to see shape: Lessons from a simplified visual worldSemantic proximity Read

Human shape representations are not an emergent property of learning to classify objectsSemantic proximity Read

The attentive reconstruction of objects facilitates robust object recognitionSemantic proximity Read

The representational hierarchy in human and artificial visual systems in the presence of object-scene regularitiesSemantic proximity Read

Behavioral signatures of face perception emerge in deep neural networks optimized for face recognitionSemantic proximity Read

Biological convolutions improve DNN robustness to noise and generalisationSemantic proximity Read

Leveraging prior concept learning improves ability to generalize from few examples in computational models of human object recognitionSemantic proximity Read

A neuronal code for object representation and memory in the human amygdala and hippocampusSemantic proximity Read

A Feedforward Convolutional Neural Network Learns from Images to Covertly Attend to Contextual Cues like Human and Bayesian Ideal ObserversSemantic proximity Read

Deep Problems with Neural Network Models of Human VisionSemantic proximity Read

The role of capacity constraints in Convolutional Neural Networks for learning random versus natural dataSemantic proximity Read

Objects, Faces, and Spaces: Organizational Principles of Visual Object Perception as Evidenced by Individual Differences in BehaviorSemantic proximity Read