20,503 research outputs found
Exploring cognitive issues in visual information retrieval
A study was conducted that compared user performance across a range of search tasks supported by both a textual and a visual information retrieval interface (VIRI). Test scores representing seven distinct cognitive abilities were examined in relation to user performance. Results indicate that, when using VIRIs, visual-perceptual abilities account for significant amounts of within-subjects variance, particularly when the relevance criteria were highly specific. Visualisation ability also seemed to be a critical factor when users were
required to change topical perspective within the visualisation. Suggestions are made for navigational cues that may help to reduce the effects of these individual differences
Combining Visual and Textual Features for Semantic Segmentation of Historical Newspapers
The massive amounts of digitized historical documents acquired over the last
decades naturally lend themselves to automatic processing and exploration.
Research work seeking to automatically process facsimiles and extract
information thereby are multiplying with, as a first essential step, document
layout analysis. If the identification and categorization of segments of
interest in document images have seen significant progress over the last years
thanks to deep learning techniques, many challenges remain with, among others,
the use of finer-grained segmentation typologies and the consideration of
complex, heterogeneous documents such as historical newspapers. Besides, most
approaches consider visual features only, ignoring textual signal. In this
context, we introduce a multimodal approach for the semantic segmentation of
historical newspapers that combines visual and textual features. Based on a
series of experiments on diachronic Swiss and Luxembourgish newspapers, we
investigate, among others, the predictive power of visual and textual features
and their capacity to generalize across time and sources. Results show
consistent improvement of multimodal models in comparison to a strong visual
baseline, as well as better robustness to high material variance
Recommended from our members
Proceedings ICPW'07: 2nd International Conference on the Pragmatic Web, 22-23 Oct. 2007, Tilburg: NL
Proceedings ICPW'07: 2nd International Conference on the Pragmatic Web, 22-23 Oct. 2007, Tilburg: N
The growth and form of knowledge networks by kinesthetic curiosity
Throughout life, we might seek a calling, companions, skills, entertainment,
truth, self-knowledge, beauty, and edification. The practice of curiosity can
be viewed as an extended and open-ended search for valuable information with
hidden identity and location in a complex space of interconnected information.
Despite its importance, curiosity has been challenging to computationally model
because the practice of curiosity often flourishes without specific goals,
external reward, or immediate feedback. Here, we show how network science,
statistical physics, and philosophy can be integrated into an approach that
coheres with and expands the psychological taxonomies of specific-diversive and
perceptual-epistemic curiosity. Using this interdisciplinary approach, we
distill functional modes of curious information seeking as searching movements
in information space. The kinesthetic model of curiosity offers a vibrant
counterpart to the deliberative predictions of model-based reinforcement
learning. In doing so, this model unearths new computational opportunities for
identifying what makes curiosity curious
Spatial and Temporal Hierarchy for Autonomous Navigation using Active Inference in Minigrid Environment
Robust evidence suggests that humans explore their environment using a
combination of topological landmarks and coarse-grained path integration. This
approach relies on identifiable environmental features (topological landmarks)
in tandem with estimations of distance and direction (coarse-grained path
integration) to construct cognitive maps of the surroundings. This cognitive
map is believed to exhibit a hierarchical structure, allowing efficient
planning when solving complex navigation tasks. Inspired by human behaviour,
this paper presents a scalable hierarchical active inference model for
autonomous navigation, exploration, and goal-oriented behaviour. The model uses
visual observation and motion perception to combine curiosity-driven
exploration with goal-oriented behaviour. Motion is planned using different
levels of reasoning, i.e., from context to place to motion. This allows for
efficient navigation in new spaces and rapid progress toward a target. By
incorporating these human navigational strategies and their hierarchical
representation of the environment, this model proposes a new solution for
autonomous navigation and exploration. The approach is validated through
simulations in a mini-grid environment.Comment: arXiv admin note: text overlap with arXiv:2309.0986
Past, Present, and Future of Simultaneous Localization And Mapping: Towards the Robust-Perception Age
Simultaneous Localization and Mapping (SLAM)consists in the concurrent
construction of a model of the environment (the map), and the estimation of the
state of the robot moving within it. The SLAM community has made astonishing
progress over the last 30 years, enabling large-scale real-world applications,
and witnessing a steady transition of this technology to industry. We survey
the current state of SLAM. We start by presenting what is now the de-facto
standard formulation for SLAM. We then review related work, covering a broad
set of topics including robustness and scalability in long-term mapping, metric
and semantic representations for mapping, theoretical performance guarantees,
active SLAM and exploration, and other new frontiers. This paper simultaneously
serves as a position paper and tutorial to those who are users of SLAM. By
looking at the published research with a critical eye, we delineate open
challenges and new research issues, that still deserve careful scientific
investigation. The paper also contains the authors' take on two questions that
often animate discussions during robotics conferences: Do robots need SLAM? and
Is SLAM solved
- …