82 citations · 324 across the 42 of their papers we have counts for
48 papers
Statistical Challenges with Dataset Construction: Why You Will Never Have Enough Images
Josh Goldman, John K. Tsotsos
Deep neural networks have achieved impressive performance on many computer vision benchmarks in recent years. However, can we be confident that impressive performance on benchmarks…
The Psychophysics of Human Three-Dimensional Active Visuospatial Problem-Solving
Markus D. Solbach, John K. Tsotsos
Our understanding of how visual systems detect, analyze and interpret visual stimuli has advanced greatly. However, the visual systems of all animals do much more; they enable visu…
Self-attention in Vision Transformers Performs Perceptual Grouping, Not Attention
Paria Mehrani, John K. Tsotsos
Recently, a considerable number of studies in computer vision involves deep neural architectures called vision transformers. Visual processing in these models incorporates computat…
Learning a model of shape selectivity in V4 cells reveals shape encoding mechanisms in the brain
Paria Mehrani, John K. Tsotsos
The mechanisms involved in transforming early visual signals to curvature representations in V4 are unknown. We propose a hierarchical model that reveals V1/V2 encodings that are e…
Next-Best-View Estimation based on Deep Reinforcement Learning for Active Object Classification
Christian Korbach, Markus D. Solbach, Raphael Memmesheimer +2
The presentation and analysis of image data from a single viewpoint are often not sufficient to solve a task. Several viewpoints are necessary to obtain more information. The next-…
Active Observer Visual Problem-Solving Methods are Dynamically Hypothesized, Deployed and Tested
Markus D. Solbach, John K. Tsotsos
The STAR architecture was designed to test the value of the full Selective Tuning model of visual attention for complex real-world visuospatial tasks and behaviors. However, knowle…
Probing the Effect of Selection Bias on Generalization: A Thought Experiment
John K. Tsotsos, Jun Luo
Learned systems in the domain of visual recognition and cognition impress in part because even though they are trained with datasets many orders of magnitude smaller than the full…
Blocks World Revisited: The Effect of Self-Occlusion on Classification by Convolutional Neural Networks
Markus D. Solbach, John K. Tsotsos
Despite the recent successes in computer vision, there remain new avenues to explore. In this work, we propose a new dataset to investigate the effect of self-occlusion on deep neu…
Video action recognition for lane-change classification and prediction of surrounding vehicles
Mahdi Biparva, David Fernández-Llorca, Rubén Izquierdo-Gonzalo +1
In highway scenarios, an alert human driver will typically anticipate early cut-in/cut-out maneuvers of surrounding vehicles using visual cues mainly. Autonomous vehicles must anti…
An Empirical Method to Quantify the Peripheral Performance Degradation in Deep Networks
Calden Wloka, John K. Tsotsos
When applying a convolutional kernel to an image, if the output is to remain the same size as the input then some form of padding is required around the image boundary, meaning tha…
Contextual Interference Reduction by Selective Fine-Tuning of Neural Networks
Mahdi Biparva, John Tsotsos
Feature disentanglement of the foreground target objects and the background surrounding context has not been yet fully accomplished. The lack of network interpretability prevents a…
PESAO: Psychophysical Experimental Setup for Active Observers
Markus D. Solbach, John K. Tsotsos
Most past and present research in computer vision involves passively observed data. Humans, however, are active observers outside the lab; they explore, search, select what and how…
Two-Stream Networks for Lane-Change Prediction of Surrounding Vehicles
David Fernández-Llorca, Mahdi Biparva, Rubén Izquierdo-Gonzalo +1
In highway scenarios, an alert human driver will typically anticipate early cut-in and cut-out maneuvers of surrounding vehicles using only visual cues. An automated system must an…
Pedestrian Action Anticipation using Contextual Feature Fusion in Stacked RNNs
Amir Rasouli, Iuliia Kotseruba, John K. Tsotsos
One of the major challenges for autonomous vehicles in urban environments is to understand and predict other road users' actions, in particular, pedestrians at the point of crossin…
Compact Neural Representation Using Attentive Network Pruning
Mahdi Biparva, John Tsotsos
Deep neural networks have evolved to become power demanding and consequently difficult to apply to small-size mobile platforms. Network parameter reduction methods have been introd…
Do Saliency Models Detect Odd-One-Out Targets? New Datasets and Evaluations
Iuliia Kotseruba, Calden Wloka, Amir Rasouli +1
Recent advances in the field of saliency have concentrated on fixation prediction, with benchmarks reaching saturation. However, there is an extensive body of works in psychology a…
Selective Segmentation Networks Using Top-Down Attention
Mahdi Biparva, John Tsotsos
Convolutional neural networks model the transformation of the input sensory data at the bottom of a network hierarchy to the semantic information at the top of the visual hierarchy…
A Possible Reason for why Data-Driven Beats Theory-Driven Computer Vision
John K. Tsotsos, Iuliia Kotseruba, Alexander Andreopoulos +1
Why do some continue to wonder about the success and dominance of deep learning methods in computer vision and AI? Is it not enough that these methods provide practical solutions t…
Scene Classification in Indoor Environments for Robots using Context Based Word Embeddings
Bao Xin Chen, Raghavender Sahdev, Dekun Wu +3
Scene Classification has been addressed with numerous techniques in computer vision literature. However, with the increasing number of scene classes in datasets in the field, it ha…
Multiplicative modulations in hue-selective cells enhance unique hue representation
Paria Mehrani, Andrei Mouraviev, John K. Tsotsos
There is still much to understand about the color processing mechanisms in the brain and the transformation from cone-opponent representations to perceptual hues. Moreover, it is u…
Fast Visual Object Tracking with Rotated Bounding Boxes
Bao Xin Chen, John K. Tsotsos
In this paper, we demonstrate a novel algorithm that uses ellipse fitting to estimate the bounding box rotation angle and size with the segmentation(mask) on the target for online…
High-Level Perceptual Similarity is Enabled by Learning Diverse Tasks
Amir Rosenfeld, Richard Zemel, John K. Tsotsos
Predicting human perceptual similarity is a challenging subject of ongoing research. The visual process underlying this aspect of human vision is thought to employ multiple differe…
Rapid Visual Categorization is not Guided by Early Salience-Based Selection
John K. Tsotsos, Iuliia Kotseruba, Calden Wloka
The current dominant visual processing paradigm in both human and machine research is the feedforward, layered hierarchy of neural-like processing elements. Within this paradigm, v…
Early recurrence enables figure border ownership
Paria Mehrani, John K. Tsotsos
The face-vase illusion introduced by Rubin demonstrates how one can switch back and forth between two different interpretations depending on how the figure outlines are assigned [1…