3,301 research outputs found
Contextual Media Retrieval Using Natural Language Queries
The widespread integration of cameras in hand-held and head-worn devices as
well as the ability to share content online enables a large and diverse visual
capture of the world that millions of users build up collectively every day. We
envision these images as well as associated meta information, such as GPS
coordinates and timestamps, to form a collective visual memory that can be
queried while automatically taking the ever-changing context of mobile users
into account. As a first step towards this vision, in this work we present
Xplore-M-Ego: a novel media retrieval system that allows users to query a
dynamic database of images and videos using spatio-temporal natural language
queries. We evaluate our system using a new dataset of real user queries as
well as through a usability study. One key finding is that there is a
considerable amount of inter-user variability, for example in the resolution of
spatial relations in natural language utterances. We show that our retrieval
system can cope with this variability using personalisation through an online
learning-based retrieval formulation.Comment: 8 pages, 9 figures, 1 tabl
Towards an Indexical Model of Situated Language Comprehension for Cognitive Agents in Physical Worlds
We propose a computational model of situated language comprehension based on
the Indexical Hypothesis that generates meaning representations by translating
amodal linguistic symbols to modal representations of beliefs, knowledge, and
experience external to the linguistic system. This Indexical Model incorporates
multiple information sources, including perceptions, domain knowledge, and
short-term and long-term experiences during comprehension. We show that
exploiting diverse information sources can alleviate ambiguities that arise
from contextual use of underspecific referring expressions and unexpressed
argument alternations of verbs. The model is being used to support linguistic
interactions in Rosie, an agent implemented in Soar that learns from
instruction.Comment: Advances in Cognitive Systems 3 (2014
How a General-Purpose Commonsense Ontology can Improve Performance of Learning-Based Image Retrieval
The knowledge representation community has built general-purpose ontologies
which contain large amounts of commonsense knowledge over relevant aspects of
the world, including useful visual information, e.g.: "a ball is used by a
football player", "a tennis player is located at a tennis court". Current
state-of-the-art approaches for visual recognition do not exploit these
rule-based knowledge sources. Instead, they learn recognition models directly
from training examples. In this paper, we study how general-purpose
ontologies---specifically, MIT's ConceptNet ontology---can improve the
performance of state-of-the-art vision systems. As a testbed, we tackle the
problem of sentence-based image retrieval. Our retrieval approach incorporates
knowledge from ConceptNet on top of a large pool of object detectors derived
from a deep learning technique. In our experiments, we show that ConceptNet can
improve performance on a common benchmark dataset. Key to our performance is
the use of the ESPGAME dataset to select visually relevant relations from
ConceptNet. Consequently, a main conclusion of this work is that
general-purpose commonsense ontologies improve performance on visual reasoning
tasks when properly filtered to select meaningful visual relations.Comment: Accepted in IJCAI-1
Context-Aware Entity Grounding with Open-Vocabulary 3D Scene Graphs
We present an Open-Vocabulary 3D Scene Graph (OVSG), a formal framework for
grounding a variety of entities, such as object instances, agents, and regions,
with free-form text-based queries. Unlike conventional semantic-based object
localization approaches, our system facilitates context-aware entity
localization, allowing for queries such as ``pick up a cup on a kitchen table"
or ``navigate to a sofa on which someone is sitting". In contrast to existing
research on 3D scene graphs, OVSG supports free-form text input and
open-vocabulary querying. Through a series of comparative experiments using the
ScanNet dataset and a self-collected dataset, we demonstrate that our proposed
approach significantly surpasses the performance of previous semantic-based
localization techniques. Moreover, we highlight the practical application of
OVSG in real-world robot navigation and manipulation experiments.Comment: The code and dataset used for evaluation can be found at
https://github.com/changhaonan/OVSG}{https://github.com/changhaonan/OVSG.
This paper has been accepted by CoRL202
From images via symbols to contexts: using augmented reality for interactive model acquisition
Systems that perform in real environments need to bind the internal state to externally
perceived objects, events, or complete scenes. How to learn this correspondence has been a long
standing problem in computer vision as well as artificial intelligence. Augmented Reality provides
an interesting perspective on this problem because a human user can directly relate displayed
system results to real environments. In the following we present a system that is able to bootstrap
internal models from user-system interactions. Starting from pictorial representations it learns
symbolic object labels that provide the basis for storing observed episodes. In a second step, more
complex relational information is extracted from stored episodes that enables the system to react
on specific scene contexts
CHORUS Deliverable 2.1: State of the Art on Multimedia Search Engines
Based on the information provided by European projects and national initiatives related to multimedia search as well as domains experts that participated in the CHORUS Think-thanks and workshops, this document reports on the state of the art related to multimedia content search from, a technical, and socio-economic perspective.
The technical perspective includes an up to date view on content based indexing and retrieval technologies, multimedia search in the context of mobile devices and peer-to-peer networks, and an overview of current evaluation and benchmark inititiatives to measure the performance of multimedia search engines.
From a socio-economic perspective we inventorize the impact and legal consequences of these technical advances and point out future directions of research
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