82 citations · 225 across the 21 of their papers we have counts for
17 papers · 1 filter
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.…
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…
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…
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…