35,311 research outputs found
A semantic and language-based representation of an environmental scene
The modeling of a landscape environment is a cognitive activity that requires appropriate spatial representations. The research presented in this paper introduces a structural and semantic categorization of a landscape view based on panoramic photographs that act as a substitute of a given natural environment. Verbal descriptions of a landscape scene provide themodeling input of our approach. This structure-based model identifies the spatial, relational, and semantic constructs that emerge from these descriptions. Concepts in the environment are qualified according to a semantic classification, their proximity and direction to the observer, and the spatial relations that qualify them. The resulting model is represented in a way that constitutes a modeling support for the study of environmental scenes, and a contribution for further research oriented to the mapping of a verbal description onto a geographical information system-based representation
A Visibility and Spatial Constraint-Based Approach for Geopositioning
Over the past decade, automated systems dedicated to geopositioning have been the object of considerable development. Despite the success of these systems for many applications, they cannot be directly applied to qualitative descriptions of space. The research presented in this paper introduces a visibility and constraintbased approach whose objective is to locate an observer from the verbal description of his/her surroundings. The geopositioning process is formally supported by a constraint-satisfaction algorithm. Preliminary experiments are applied to the description of environmental scenes
A Visibility and Spatial Constraint-Based Approach for Geopositioning
Over the past decade, automated systems dedicated to geopositioning have been the object of considerable development. Despite the success of these systems for many applications, they cannot be directly applied to qualitative descriptions of space. The research presented in this paper introduces a visibility and constraintbased approach whose objective is to locate an observer from the verbal description of his/her surroundings. The geopositioning process is formally supported by a constraint-satisfaction algorithm. Preliminary experiments are applied to the description of environmental scenes
ImageSpirit: Verbal Guided Image Parsing
Humans describe images in terms of nouns and adjectives while algorithms
operate on images represented as sets of pixels. Bridging this gap between how
humans would like to access images versus their typical representation is the
goal of image parsing, which involves assigning object and attribute labels to
pixel. In this paper we propose treating nouns as object labels and adjectives
as visual attribute labels. This allows us to formulate the image parsing
problem as one of jointly estimating per-pixel object and attribute labels from
a set of training images. We propose an efficient (interactive time) solution.
Using the extracted labels as handles, our system empowers a user to verbally
refine the results. This enables hands-free parsing of an image into pixel-wise
object/attribute labels that correspond to human semantics. Verbally selecting
objects of interests enables a novel and natural interaction modality that can
possibly be used to interact with new generation devices (e.g. smart phones,
Google Glass, living room devices). We demonstrate our system on a large number
of real-world images with varying complexity. To help understand the tradeoffs
compared to traditional mouse based interactions, results are reported for both
a large scale quantitative evaluation and a user study.Comment: http://mmcheng.net/imagespirit
Language-Based Image Editing with Recurrent Attentive Models
We investigate the problem of Language-Based Image Editing (LBIE). Given a
source image and a natural language description, we want to generate a target
image by editing the source image based on the description. We propose a
generic modeling framework for two sub-tasks of LBIE: language-based image
segmentation and image colorization. The framework uses recurrent attentive
models to fuse image and language features. Instead of using a fixed step size,
we introduce for each region of the image a termination gate to dynamically
determine after each inference step whether to continue extrapolating
additional information from the textual description. The effectiveness of the
framework is validated on three datasets. First, we introduce a synthetic
dataset, called CoSaL, to evaluate the end-to-end performance of our LBIE
system. Second, we show that the framework leads to state-of-the-art
performance on image segmentation on the ReferIt dataset. Third, we present the
first language-based colorization result on the Oxford-102 Flowers dataset.Comment: Accepted to CVPR 2018 as a Spotligh
AI: Inventing a new kind of machine.
A means-ends approach to engineering an artificial intelligence machine now suggests that we focus on the differences between human capabilities and the best computer programs. These differences suggest two basic limitations in the "symbolic" approach. First, human memory is much more than a storehouse where structures are put away, indexed, and rotely retrieved. Second, human reasoning involves more than searching, matching, and recombining previously stored descriptions of situations and action plans. Indeed, these hypotheses are related: Remembering and reasoning both involve reconceptualization. This short paper outlines recent work in situated cognition, robotics, and neural networks that suggests we frame the problem if AI in terms of inventing a new kind of machine
The Case for Dynamic Models of Learners' Ontologies in Physics
In a series of well-known papers, Chi and Slotta (Chi, 1992; Chi & Slotta,
1993; Chi, Slotta & de Leeuw, 1994; Slotta, Chi & Joram, 1995; Chi, 2005;
Slotta & Chi, 2006) have contended that a reason for students' difficulties in
learning physics is that they think about concepts as things rather than as
processes, and that there is a significant barrier between these two
ontological categories. We contest this view, arguing that expert and novice
reasoning often and productively traverses ontological categories. We cite
examples from everyday, classroom, and professional contexts to illustrate
this. We agree with Chi and Slotta that instruction should attend to learners'
ontologies; but we find these ontologies are better understood as dynamic and
context-dependent, rather than as static constraints. To promote one
ontological description in physics instruction, as suggested by Slotta and Chi,
could undermine novices' access to productive cognitive resources they bring to
their studies and inhibit their transition to the dynamic ontological
flexibility required of experts.Comment: The Journal of the Learning Sciences (In Press
Understanding contextual interactions to design navigational context-aware applications
Context-aware technology has stimulated rigorous research into novel ways to support people in a wide range of tasks and situations. However, the effectiveness of these technologies will ultimately be dependent on the extent to which contextual interactions are understood and accounted for in their design. This study involved an investigation of contextual interactions required for route navigation. The purpose was to illustrate the heterogeneous nature of humans in interaction with their environmental context. Participants were interviewed to determine how each interacts with or use objects/information in the environment in which to navigate/orientate. Results revealed that people vary individually and collectively. Usability implications for the design of navigational context-aware applications are identified and discussed
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