527 research outputs found
Adversarial Attacks and Defenses in Machine Learning-Powered Networks: A Contemporary Survey
Adversarial attacks and defenses in machine learning and deep neural network
have been gaining significant attention due to the rapidly growing applications
of deep learning in the Internet and relevant scenarios. This survey provides a
comprehensive overview of the recent advancements in the field of adversarial
attack and defense techniques, with a focus on deep neural network-based
classification models. Specifically, we conduct a comprehensive classification
of recent adversarial attack methods and state-of-the-art adversarial defense
techniques based on attack principles, and present them in visually appealing
tables and tree diagrams. This is based on a rigorous evaluation of the
existing works, including an analysis of their strengths and limitations. We
also categorize the methods into counter-attack detection and robustness
enhancement, with a specific focus on regularization-based methods for
enhancing robustness. New avenues of attack are also explored, including
search-based, decision-based, drop-based, and physical-world attacks, and a
hierarchical classification of the latest defense methods is provided,
highlighting the challenges of balancing training costs with performance,
maintaining clean accuracy, overcoming the effect of gradient masking, and
ensuring method transferability. At last, the lessons learned and open
challenges are summarized with future research opportunities recommended.Comment: 46 pages, 21 figure
Multi-scale Interactive Network for Salient Object Detection
Deep-learning based salient object detection methods achieve great progress.
However, the variable scale and unknown category of salient objects are great
challenges all the time. These are closely related to the utilization of
multi-level and multi-scale features. In this paper, we propose the aggregate
interaction modules to integrate the features from adjacent levels, in which
less noise is introduced because of only using small up-/down-sampling rates.
To obtain more efficient multi-scale features from the integrated features, the
self-interaction modules are embedded in each decoder unit. Besides, the class
imbalance issue caused by the scale variation weakens the effect of the binary
cross entropy loss and results in the spatial inconsistency of the predictions.
Therefore, we exploit the consistency-enhanced loss to highlight the
fore-/back-ground difference and preserve the intra-class consistency.
Experimental results on five benchmark datasets demonstrate that the proposed
method without any post-processing performs favorably against 23
state-of-the-art approaches. The source code will be publicly available at
https://github.com/lartpang/MINet.Comment: Accepted by CVPR 202
Salient Object Detection Techniques in Computer Vision-A Survey.
Detection and localization of regions of images that attract immediate human visual attention is currently an intensive area of research in computer vision. The capability of automatic identification and segmentation of such salient image regions has immediate consequences for applications in the field of computer vision, computer graphics, and multimedia. A large number of salient object detection (SOD) methods have been devised to effectively mimic the capability of the human visual system to detect the salient regions in images. These methods can be broadly categorized into two categories based on their feature engineering mechanism: conventional or deep learning-based. In this survey, most of the influential advances in image-based SOD from both conventional as well as deep learning-based categories have been reviewed in detail. Relevant saliency modeling trends with key issues, core techniques, and the scope for future research work have been discussed in the context of difficulties often faced in salient object detection. Results are presented for various challenging cases for some large-scale public datasets. Different metrics considered for assessment of the performance of state-of-the-art salient object detection models are also covered. Some future directions for SOD are presented towards end
Multimodal Computational Attention for Scene Understanding
Robotic systems have limited computational capacities. Hence, computational attention models are important to focus on specific stimuli and allow for complex cognitive processing. For this purpose, we developed auditory and visual attention models that enable robotic platforms to efficiently explore and analyze natural scenes. To allow for attention guidance in human-robot interaction, we use machine learning to integrate the influence of verbal and non-verbal social signals into our models
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