461,780 research outputs found
Conditional Cross Attention Network for Multi-Space Embedding without Entanglement in Only a SINGLE Network
Many studies in vision tasks have aimed to create effective embedding spaces
for single-label object prediction within an image. However, in reality, most
objects possess multiple specific attributes, such as shape, color, and length,
with each attribute composed of various classes. To apply models in real-world
scenarios, it is essential to be able to distinguish between the granular
components of an object. Conventional approaches to embedding multiple specific
attributes into a single network often result in entanglement, where
fine-grained features of each attribute cannot be identified separately. To
address this problem, we propose a Conditional Cross-Attention Network that
induces disentangled multi-space embeddings for various specific attributes
with only a single backbone. Firstly, we employ a cross-attention mechanism to
fuse and switch the information of conditions (specific attributes), and we
demonstrate its effectiveness through a diverse visualization example.
Secondly, we leverage the vision transformer for the first time to a
fine-grained image retrieval task and present a simple yet effective framework
compared to existing methods. Unlike previous studies where performance varied
depending on the benchmark dataset, our proposed method achieved consistent
state-of-the-art performance on the FashionAI, DARN, DeepFashion, and Zappos50K
benchmark datasets.Comment: ICCV 2023 Accepte
mixed attention auto encoder for multi-class industrial anomaly detection
Most existing methods for unsupervised industrial anomaly detection train a
separate model for each object category. This kind of approach can easily
capture the category-specific feature distributions, but results in high
storage cost and low training efficiency. In this paper, we propose a unified
mixed-attention auto encoder (MAAE) to implement multi-class anomaly detection
with a single model. To alleviate the performance degradation due to the
diverse distribution patterns of different categories, we employ spatial
attentions and channel attentions to effectively capture the global category
information and model the feature distributions of multiple classes.
Furthermore, to simulate the realistic noises on features and preserve the
surface semantics of objects from different categories which are essential for
detecting the subtle anomalies, we propose an adaptive noise generator and a
multi-scale fusion module for the pre-trained features. MAAE delivers
remarkable performances on the benchmark dataset compared with the
state-of-the-art methods.Comment: 5 pages, 4 figure
The Space Object Ontology
Achieving space domain awareness requires the
identification, characterization, and tracking of space objects.
Storing and leveraging associated space object data for purposes
such as hostile threat assessment, object identification, and
collision prediction and avoidance present further challenges.
Space objects are characterized according to a variety of
parameters including their identifiers, design specifications,
components, subsystems, capabilities, vulnerabilities, origins,
missions, orbital elements, patterns of life, processes, operational
statuses, and associated persons, organizations, or nations. The
Space Object Ontology provides a consensus-based realist
framework for formulating such characterizations in a
computable fashion. Space object data are aligned with classes
and relations in the Space Object Ontology and stored in a
dynamically updated Resource Description Framework triple
store, which can be queried to support space domain awareness
and the needs of spacecraft operators. This paper presents the
core of the Space Object Ontology, discusses its advantages over
other approaches to space object classification, and demonstrates
its ability to combine diverse sets of data from multiple sources
within an expandable framework. Finally, we show how the
ontology provides benefits for enhancing and maintaining longterm
space domain awareness
Revisiting knowledge transfer for training object class detectors
We propose to revisit knowledge transfer for training object detectors on
target classes from weakly supervised training images, helped by a set of
source classes with bounding-box annotations. We present a unified knowledge
transfer framework based on training a single neural network multi-class object
detector over all source classes, organized in a semantic hierarchy. This
generates proposals with scores at multiple levels in the hierarchy, which we
use to explore knowledge transfer over a broad range of generality, ranging
from class-specific (bicycle to motorbike) to class-generic (objectness to any
class). Experiments on the 200 object classes in the ILSVRC 2013 detection
dataset show that our technique: (1) leads to much better performance on the
target classes (70.3% CorLoc, 36.9% mAP) than a weakly supervised baseline
which uses manually engineered objectness [11] (50.5% CorLoc, 25.4% mAP). (2)
delivers target object detectors reaching 80% of the mAP of their fully
supervised counterparts. (3) outperforms the best reported transfer learning
results on this dataset (+41% CorLoc and +3% mAP over [18, 46], +16.2% mAP over
[32]). Moreover, we also carry out several across-dataset knowledge transfer
experiments [27, 24, 35] and find that (4) our technique outperforms the weakly
supervised baseline in all dataset pairs by 1.5x-1.9x, establishing its general
applicability.Comment: CVPR 1
Implementing a map based simulator for the location API for J2ME
The Java Location API for J2METM integrates generic positioning and orientation data with
persistent storage of landmark objects. It can be used to develop location based service
applications for small mobile devices, and these applications can be tested using simulation
environments. Currently the only simulation tools in the public domain are proprietary
mobile device simulators that are driven by GPS data log files, but it is sometimes useful to
be able to test location based services using interactive map-based tools. In addition, we
may need to experiment with extensions and changes to the standard API to support
additional services, requiring an open source environment. In this paper we describe the
implementation of an open source map-based simulation tool compatible with other
commonly used development and deployment tools
Enthusing and inspiring with reusable kinaesthetic activities
We describe the experiences of three University projects that use a style of physical, non-computer based activity to enthuse and teach school students computer science concepts. We show that this kind of activity is effective as an outreach and teaching resource even when reused across different age/ability ranges, in lecture and workshop formats and for delivery by different people. We introduce the concept of a Reusable Outreach Object (ROO) that extends Reusable Learning Objects. and argue for a community effort in developing a repository of such objects
A review of associative classification mining
Associative classification mining is a promising approach in data mining that utilizes the
association rule discovery techniques to construct classification systems, also known as
associative classifiers. In the last few years, a number of associative classification algorithms
have been proposed, i.e. CPAR, CMAR, MCAR, MMAC and others. These algorithms
employ several different rule discovery, rule ranking, rule pruning, rule prediction and rule
evaluation methods. This paper focuses on surveying and comparing the state-of-the-art associative
classification techniques with regards to the above criteria. Finally, future directions in associative
classification, such as incremental learning and mining low-quality data sets, are also
highlighted in this paper
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