82 citations · 225 across the 21 of their papers we have counts for
24 papers
Disentangling Pictorial Cue Understanding from Language Bias in VLMs via Depth Ordering Task
Yiqian Liu, Iuliia Kotseruba, John K. Tsotsos
In this paper, we study depth perception of vision-language models (VLMs) to isolate the effects of pictorial depth cues and disentangle vision and language influences on model per…
SNAP: A Benchmark for Testing the Effects of Capture Conditions on Fundamental Vision Tasks
Iuliia Kotseruba, John K. Tsotsos
Generalization of deep-learning-based (DL) computer vision algorithms to various image perturbations is hard to establish and remains an active area of research. The majority of pa…
Diving Deeper Into Pedestrian Behavior Understanding: Intention Estimation, Action Prediction, and Event Risk Assessment
Amir Rasouli, Iuliia Kotseruba
In this paper, we delve into the pedestrian behavior understanding problem from the perspective of three different tasks: intention estimation, action prediction, and event risk as…
SCOUT+: Towards Practical Task-Driven Drivers' Gaze Prediction
Iuliia Kotseruba, John K. Tsotsos
Accurate prediction of drivers' gaze is an important component of vision-based driver monitoring and assistive systems. Of particular interest are safety-critical episodes, such as…
Data Limitations for Modeling Top-Down Effects on Drivers' Attention
Iuliia Kotseruba, John K. Tsotsos
Driving is a visuomotor task, i.e., there is a connection between what drivers see and what they do. While some models of drivers' gaze account for top-down effects of drivers' act…
Understanding and Modeling the Effects of Task and Context on Drivers' Gaze Allocation
Iuliia Kotseruba, John K. Tsotsos
To further advance driver monitoring and assistance systems, it is important to understand how drivers allocate their attention, in other words, where do they tend to look and why.…
Intend-Wait-Perceive-Cross: Exploring the Effects of Perceptual Limitations on Pedestrian Decision-Making
Iuliia Kotseruba, Amir Rasouli
Current research on pedestrian behavior understanding focuses on the dynamics of pedestrians and makes strong assumptions about their perceptual abilities. For instance, it is ofte…
NeurIPS 2022 Competition: Driving SMARTS
Amir Rasouli, Randy Goebel, Matthew E. Taylor +15
Driving SMARTS is a regular competition designed to tackle problems caused by the distribution shift in dynamic interaction contexts that are prevalent in real-world autonomous dri…
PedFormer: Pedestrian Behavior Prediction via Cross-Modal Attention Modulation and Gated Multitask Learning
Amir Rasouli, Iuliia Kotseruba
Predicting pedestrian behavior is a crucial task for intelligent driving systems. Accurate predictions require a deep understanding of various contextual elements that potentially…
Intend-Wait-Cross: Towards Modeling Realistic Pedestrian Crossing Behavior
Amir Rasouli, Iuliia Kotseruba
In this paper, we present a microscopic agent-based pedestrian behavior model Intend-Wait-Cross. The model is comprised of rules representing behaviors of pedestrians as a series o…
Industry and Academic Research in Computer Vision
Iuliia Kotseruba, Manos Papagelis, John K. Tsotsos
This work aims to study the dynamic between research in the industry and academia in computer vision. The results are demonstrated on a set of top-5 vision conferences that are rep…
Behavioral Research and Practical Models of Drivers' Attention
Iuliia Kotseruba, John K. Tsotsos
Driving is a routine activity for many, but it is far from simple. Drivers deal with multiple concurrent tasks, such as keeping the vehicle in the lane, observing and anticipating…
On the Control of Attentional Processes in Vision
John K. Tsotsos, Omar Abid, Iuliia Kotseruba +1
The study of attentional processing in vision has a long and deep history. Recently, several papers have presented insightful perspectives into how the coordination of multiple att…
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…
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…
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…
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…
SMILER: Saliency Model Implementation Library for Experimental Research
Calden Wloka, Toni Kunić, Iuliia Kotseruba +4
The Saliency Model Implementation Library for Experimental Research (SMILER) is a new software package which provides an open, standardized, and extensible framework for maintainin…
Visual Attention and its Intimate Links to Spatial Cognition
John K. Tsotsos, Iuliia Kotseruba, Amir Rasouli +1
It is almost universal to regard attention as the facility that permits an agent, human or machine, to give priority processing resources to relevant stimuli while ignoring the irr…
Saccade Sequence Prediction: Beyond Static Saliency Maps
Calden Wloka, Iuliia Kotseruba, John K. Tsotsos
Visual attention is a field with a considerable history, with eye movement control and prediction forming an important subfield. Fixation modeling in the past decades has been larg…
STAR-RT: Visual attention for real-time video game playing
Iuliia Kotseruba, John K. Tsotsos
In this paper we present STAR-RT - the first working prototype of Selective Tuning Attention Reference (STAR) model and Cognitive Programs (CPs). The Selective Tuning (ST) model re…
Agreeing to Cross: How Drivers and Pedestrians Communicate
Amir Rasouli, Iuliia Kotseruba, John K. Tsotsos
The contribution of this paper is twofold. The first is a novel dataset for studying behaviors of traffic participants while crossing. Our dataset contains more than 650 samples of…
A Review of 40 Years of Cognitive Architecture Research: Core Cognitive Abilities and Practical Applications
Iuliia Kotseruba, John K. Tsotsos
In this paper we present a broad overview of the last 40 years of research on cognitive architectures. Although the number of existing architectures is nearing several hundred, mos…
Joint Attention in Autonomous Driving (JAAD)
Iuliia Kotseruba, Amir Rasouli, John K. Tsotsos
In this paper we present a novel dataset for a critical aspect of autonomous driving, the joint attention that must occur between drivers and of pedestrians, cyclists or other driv…