37 research outputs found
A Simple Geometric-Aware Indoor Positioning Interpolation Algorithm Based on Manifold Learning
Interpolation methodologies have been widely used within the domain of indoor
positioning systems. However, existing indoor positioning interpolation
algorithms exhibit several inherent limitations, including reliance on complex
mathematical models, limited flexibility, and relatively low precision. To
enhance the accuracy and efficiency of indoor positioning interpolation
techniques, this paper proposes a simple yet powerful geometric-aware
interpolation algorithm for indoor positioning tasks. The key to our algorithm
is to exploit the geometric attributes of the local topological manifold using
manifold learning principles. Therefore, instead of constructing complicated
mathematical models, the proposed algorithm facilitates the more precise and
efficient estimation of points grounded in the local topological manifold.
Moreover, our proposed method can be effortlessly integrated into any indoor
positioning system, thereby bolstering its adaptability. Through a systematic
array of experiments and comprehensive performance analyses conducted on both
simulated and real-world datasets, we demonstrate that the proposed algorithm
consistently outperforms the most commonly used and representative
interpolation approaches regarding interpolation accuracy and efficiency.
Furthermore, the experimental results also underscore the substantial practical
utility of our method and its potential applicability in real-time indoor
positioning scenarios
GIMM: InfoMin-Max for Automated Graph Contrastive Learning
Graph contrastive learning (GCL) shows great potential in unsupervised graph
representation learning. Data augmentation plays a vital role in GCL, and its
optimal choice heavily depends on the downstream task. Many GCL methods with
automated data augmentation face the risk of insufficient information as they
fail to preserve the essential information necessary for the downstream task.
To solve this problem, we propose InfoMin-Max for automated Graph contrastive
learning (GIMM), which prevents GCL from encoding redundant information and
losing essential information. GIMM consists of two major modules: (1) automated
graph view generator, which acquires the approximation of InfoMin's optimal
views through adversarial training without requiring task-relevant information;
(2) view comparison, which learns an excellent encoder by applying InfoMax to
view representations. To the best of our knowledge, GIMM is the first method
that combines the InfoMin and InfoMax principles in GCL. Besides, GIMM
introduces randomness to augmentation, thus stabilizing the model against
perturbations. Extensive experiments on unsupervised and semi-supervised
learning for node and graph classification demonstrate the superiority of our
GIMM over state-of-the-art GCL methods with automated and manual data
augmentation
RADAP: A Robust and Adaptive Defense Against Diverse Adversarial Patches on Face Recognition
Face recognition (FR) systems powered by deep learning have become widely
used in various applications. However, they are vulnerable to adversarial
attacks, especially those based on local adversarial patches that can be
physically applied to real-world objects. In this paper, we propose RADAP, a
robust and adaptive defense mechanism against diverse adversarial patches in
both closed-set and open-set FR systems. RADAP employs innovative techniques,
such as FCutout and F-patch, which use Fourier space sampling masks to improve
the occlusion robustness of the FR model and the performance of the patch
segmenter. Moreover, we introduce an edge-aware binary cross-entropy (EBCE)
loss function to enhance the accuracy of patch detection. We also present the
split and fill (SAF) strategy, which is designed to counter the vulnerability
of the patch segmenter to complete white-box adaptive attacks. We conduct
comprehensive experiments to validate the effectiveness of RADAP, which shows
significant improvements in defense performance against various adversarial
patches, while maintaining clean accuracy higher than that of the undefended
Vanilla model
A Long-Tail Friendly Representation Framework for Artist and Music Similarity
The investigation of the similarity between artists and music is crucial in
music retrieval and recommendation, and addressing the challenge of the
long-tail phenomenon is increasingly important. This paper proposes a Long-Tail
Friendly Representation Framework (LTFRF) that utilizes neural networks to
model the similarity relationship. Our approach integrates music, user,
metadata, and relationship data into a unified metric learning framework, and
employs a meta-consistency relationship as a regular term to introduce the
Multi-Relationship Loss. Compared to the Graph Neural Network (GNN), our
proposed framework improves the representation performance in long-tail
scenarios, which are characterized by sparse relationships between artists and
music. We conduct experiments and analysis on the AllMusic dataset, and the
results demonstrate that our framework provides a favorable generalization of
artist and music representation. Specifically, on similar artist/music
recommendation tasks, the LTFRF outperforms the baseline by 9.69%/19.42% in Hit
Ratio@10, and in long-tail cases, the framework achieves 11.05%/14.14% higher
than the baseline in Consistent@10
NeRFTAP: Enhancing Transferability of Adversarial Patches on Face Recognition using Neural Radiance Fields
Face recognition (FR) technology plays a crucial role in various
applications, but its vulnerability to adversarial attacks poses significant
security concerns. Existing research primarily focuses on transferability to
different FR models, overlooking the direct transferability to victim's face
images, which is a practical threat in real-world scenarios. In this study, we
propose a novel adversarial attack method that considers both the
transferability to the FR model and the victim's face image, called NeRFTAP.
Leveraging NeRF-based 3D-GAN, we generate new view face images for the source
and target subjects to enhance transferability of adversarial patches. We
introduce a style consistency loss to ensure the visual similarity between the
adversarial UV map and the target UV map under a 0-1 mask, enhancing the
effectiveness and naturalness of the generated adversarial face images.
Extensive experiments and evaluations on various FR models demonstrate the
superiority of our approach over existing attack techniques. Our work provides
valuable insights for enhancing the robustness of FR systems in practical
adversarial settings
Temporal Convolutional Attention-based Network For Sequence Modeling
With the development of feed-forward models, the default model for sequence
modeling has gradually evolved to replace recurrent networks. Many powerful
feed-forward models based on convolutional networks and attention mechanism
were proposed and show more potential to handle sequence modeling tasks. We
wonder that is there an architecture that can not only achieve an approximate
substitution of recurrent network, but also absorb the advantages of
feed-forward models. So we propose an exploratory architecture referred to
Temporal Convolutional Attention-based Network (TCAN) which combines temporal
convolutional network and attention mechanism. TCAN includes two parts, one is
Temporal Attention (TA) which captures relevant features inside the sequence,
the other is Enhanced Residual (ER) which extracts shallow layer's important
information and transfers to deep layers. We improve the state-of-the-art
results of bpc/perplexity to 26.92 on word-level PTB, 1.043 on character-level
PTB, and 6.66 on WikiText-2.Comment: 7 pages, 4 figure
Image Data Augmentation for Deep Learning: A Survey
Deep learning has achieved remarkable results in many computer vision tasks.
Deep neural networks typically rely on large amounts of training data to avoid
overfitting. However, labeled data for real-world applications may be limited.
By improving the quantity and diversity of training data, data augmentation has
become an inevitable part of deep learning model training with image data.
As an effective way to improve the sufficiency and diversity of training
data, data augmentation has become a necessary part of successful application
of deep learning models on image data. In this paper, we systematically review
different image data augmentation methods. We propose a taxonomy of reviewed
methods and present the strengths and limitations of these methods. We also
conduct extensive experiments with various data augmentation methods on three
typical computer vision tasks, including semantic segmentation, image
classification and object detection. Finally, we discuss current challenges
faced by data augmentation and future research directions to put forward some
useful research guidance