arXiv subjects

Iuliia Kotseruba

Publications and source records attributed to Iuliia Kotseruba.

24 records · Page 2Linked to original sources

arXiv2018

SMILER: Saliency Model Implementation Library for Experimental Research

The Saliency Model Implementation Library for Experimental Research (SMILER) is a new software package which provides an open, standardized, and extensible framework for maintaining and executing computational saliency models. This work drastically reduces the human effort required to apply saliency algorithms to new tasks and datasets, while also ensuring consistency and procedural correctness for results and conclusions produced by different parties. At its launch SMILER already includes twenty three saliency models (fourteen models based in MATLAB and nine supported through containerization), and the open design of SMILER encourages this number to grow with future contributions from the community. The project may be downloaded and contributed to through its GitHub page: https://github.com/tsotsoslab/smiler

cs.CV↗

arXiv2018

Visual Attention and its Intimate Links to Spatial Cognition

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 irrelevant. The reality of how this might manifest itself throughout all the forms of perceptual and cognitive processes possessed by humans, however, is not as clear. Here we examine this reality with a broad perspective in order to highlight the myriad ways that attentional processes impact both perception and cognition. The paper concludes by showing two real world problems that exhibit sufficient complexity to illustrate the ways in which attention and cognition connect. These then point to new avenues of research that might illuminate the overall cognitive architecture of spatial cognition.

cs.CV↗

arXiv2018

A Review of 40 Years of Cognitive Architecture Research: Core Cognitive Abilities and Practical Applications

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, most of the existing surveys do not reflect this growth and focus on a handful of well-established architectures. Thus, in this survey we wanted to shift the focus towards a more inclusive and high-level overview of the research on cognitive architectures. Our final set of 84 architectures includes 49 that are still actively developed, and borrow from a diverse set of disciplines, spanning areas from psychoanalysis to neuroscience. To keep the length of this paper within reasonable limits we discuss only the core cognitive abilities, such as perception, attention mechanisms, action selection, memory, learning and reasoning. In order to assess the breadth of practical applications of cognitive architectures we gathered information on over 900 practical projects implemented using the cognitive architectures in our list. We use various visualization techniques to highlight overall trends in the development of the field. In addition to summarizing the current state-of-the-art in the cognitive architecture research, this survey describes a variety of methods and ideas that have been tried and their relative success in modeling human cognitive abilities, as well as which aspects of cognitive behavior need more research with respect to their mechanistic counterparts and thus can further inform how cognitive science might progress.

cs.AI↗

arXiv2017

Saccade Sequence Prediction: Beyond Static Saliency Maps

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 largely dominated computationally by a number of highly influential bottom-up saliency models, such as the Itti-Koch-Niebur model. The accuracy of such models has dramatically increased recently due to deep learning. However, on static images the emphasis of these models has largely been based on non-ordered prediction of fixations through a saliency map. Very few implemented models can generate temporally ordered human-like sequences of saccades beyond an initial fixation point. Towards addressing these shortcomings we present STAR-FC, a novel multi-saccade generator based on a central/peripheral integration of deep learning-based saliency and lower-level feature-based saliency. We have evaluated our model using the CAT2000 database, successfully predicting human patterns of fixation with equivalent accuracy and quality compared to what can be achieved by using one human sequence to predict another. This is a significant improvement over fixation sequences predicted by state-of-the-art saliency algorithms.

cs.CV↗

arXiv2017

STAR-RT: Visual attention for real-time video game playing

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 received substantial support through psychological and neurophysiological experiments. The STAR framework expands ST and applies it to practical visual tasks. In order to do so, similarly to many cognitive architectures, STAR combines the visual hierarchy (based on ST) with the executive controller, working and short-term memory components and fixation controller. CPs in turn enable the communication among all these elements for visual task execution. To test the relevance of the system in a realistic context, we implemented the necessary components of STAR and designed CPs for playing two closed-source video games - Canabaltand Robot Unicorn Attack. Since both games run in a browser window, our algorithm has the same amount of information and the same amount of time to react to the events on the screen as a human player would. STAR-RT plays both games in real time using only visual input and achieves scores comparable to human expert players. It thus provides an existence proof for the utility of the particular CP structure and primitives used and the potential for continued experimentation and verification of their utility in broader scenarios.

cs.CV↗

arXiv2017

Agreeing to Cross: How Drivers and Pedestrians Communicate

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 pedestrian behaviors in various street configurations and weather conditions. These examples were selected from approx. 240 hours of driving in the city, suburban and urban roads. The second contribution is an analysis of our data from the point of view of joint attention. We identify what types of non-verbal communication cues road users use at the point of crossing, their responses, and under what circumstances the crossing event takes place. It was found that in more than 90% of the cases pedestrians gaze at the approaching cars prior to crossing in non-signalized crosswalks. The crossing action, however, depends on additional factors such as time to collision (TTC), explicit driver's reaction or structure of the crosswalk.

cs.RO↗