Object Recognition: Models

Poster Session: Wednesday, May 22, 2024, 8:30 am – 12:30 pm, Pavilion

Abstract#

Poster Title

First Author

63.401

How to estimate noise ceilings for computational models of visual cortex

Chen, Zirui

63.402

3D shape recognition in humans and deep neural networks

Fu, Shuhao

63.403

Characteristics of the emergence of category selectivity in convolutional neural networks

Verosky, Niels J.

63.404

Differential sensitivity of humans and deep networks to the amplitude and phase of shape features

Baker, Nicholas

63.405

Quantifying the Quality of Shape and Texture Representations in Deep Neural Network Models

Doshi, Fenil R.

63.406

Geometric properties of object manifolds in neural network models of visual cortex

Bonner, Michael

63.407

A biologically inspired framework for contrastive learning of visual representations: BioCLR

Han, Zhixian

63.408

Evaluating the Alignment of Machine and Human Explanations in Visual Object Recognition through a Novel Behavioral Approach

Kashef Alghetaa, Yousif

63.409

Interpreting distributed population codes with feature-accentuated visual encoding models

Prince, Jacob S.

63.410

Investigating power laws in neural network models of visual cortex

Townley, Keaton

63.411

Sparse components distinguish visual pathways and their alignment to neural networks

Marvi, Ammar

63.412

Spatial filters in neural network models of visual cortex do not need to be learned

Passi, Ananya

63.413

Spatial Frequency Decoupling: Bio-inspired strategy for Network Robustness

Arslan, Suayb

63.414

When Machines Outshine Humans in Object Recognition, Benchmarking Dilemma

Darvishi Bayazi, Mohammad Javad

63.415

Visual and auditory object recognition in relation to spatial abilities

Smithson, Conor J. R.