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Geospatial big data: Survey and challenges
In recent years, geospatial big data (GBD) has obtained attention across various disciplines,
categorized into big Earth observation data and big human behavior data. Identifying …
categorized into big Earth observation data and big human behavior data. Identifying …
On the robustness of large multimodal models against image adversarial attacks
Recent advances in instruction tuning have led to the development of State-of-the-Art Large
Multimodal Models (LMMs). Given the novelty of these models the impact of visual …
Multimodal Models (LMMs). Given the novelty of these models the impact of visual …
Computation-efficient deep learning for computer vision: A survey
Over the past decade, deep learning models have exhibited considerable advancements,
reaching or even exceeding human-level performance in a range of visual perception tasks …
reaching or even exceeding human-level performance in a range of visual perception tasks …
Ags: Affordable and generalizable substitute training for transferable adversarial attack
In practical black-box attack scenarios, most of the existing transfer-based attacks employ
pretrained models (eg ResNet50) as the substitute models. Unfortunately, these substitute …
pretrained models (eg ResNet50) as the substitute models. Unfortunately, these substitute …
Bit-mask Robust Contrastive Knowledge Distillation for Unsupervised Semantic Hashing
Unsupervised semantic hashing has emerged as an indispensable technique for fast image
search, which aims to convert images into binary hash codes without relying on labels …
search, which aims to convert images into binary hash codes without relying on labels …
Discrepancy and structure-based contrast for test-time adaptive retrieval
Domain adaptive hashing has received increasing attention since it is capable of enhancing
the performance of retrieval if the target domain for testing meets domain shift. However …
the performance of retrieval if the target domain for testing meets domain shift. However …
Deep debiased contrastive hashing
Hashing has achieved great success in multimedia retrieval due to its high computing
efficiency and low storage cost. Recently, contrastive-learning-based hashing methods have …
efficiency and low storage cost. Recently, contrastive-learning-based hashing methods have …
One-bit deep hashing: Towards resource-efficient hashing model with binary neural network
Deep Hashing (DH) has emerged as an indispensable technique for fast image search in
recent years. To deploy DH on resource-limited devices, the Binary Neural Network (BNN) …
recent years. To deploy DH on resource-limited devices, the Binary Neural Network (BNN) …
From data to optimization: Data-free deep incremental hashing with data disambiguation and adaptive proxies
Deep incremental hashing methods require a large number of original training samples to
preserve old knowledge. However, the old training samples are not always available. This …
preserve old knowledge. However, the old training samples are not always available. This …
[HTML][HTML] Performance evaluation of attention-deep hashing based medical image retrieval in brain MRI datasets
Background Brain MRI images pose significant challenges due to their complexity and
voluminous data, which often hinder the accuracy of traditional image retrieval methods. In …
voluminous data, which often hinder the accuracy of traditional image retrieval methods. In …