1,534 research outputs found
Deep Image Retrieval: A Survey
In recent years a vast amount of visual content has been generated and shared
from various fields, such as social media platforms, medical images, and
robotics. This abundance of content creation and sharing has introduced new
challenges. In particular, searching databases for similar content, i.e.content
based image retrieval (CBIR), is a long-established research area, and more
efficient and accurate methods are needed for real time retrieval. Artificial
intelligence has made progress in CBIR and has significantly facilitated the
process of intelligent search. In this survey we organize and review recent
CBIR works that are developed based on deep learning algorithms and techniques,
including insights and techniques from recent papers. We identify and present
the commonly-used benchmarks and evaluation methods used in the field. We
collect common challenges and propose promising future directions. More
specifically, we focus on image retrieval with deep learning and organize the
state of the art methods according to the types of deep network structure, deep
features, feature enhancement methods, and network fine-tuning strategies. Our
survey considers a wide variety of recent methods, aiming to promote a global
view of the field of instance-based CBIR.Comment: 20 pages, 11 figure
Towards Efficient Deep Hashing Retrieval: Condensing Your Data via Feature-Embedding Matching
The expenses involved in training state-of-the-art deep hashing retrieval
models have witnessed an increase due to the adoption of more sophisticated
models and large-scale datasets. Dataset Distillation (DD) or Dataset
Condensation(DC) focuses on generating smaller synthetic dataset that retains
the original information. Nevertheless, existing DD methods face challenges in
maintaining a trade-off between accuracy and efficiency. And the
state-of-the-art dataset distillation methods can not expand to all deep
hashing retrieval methods. In this paper, we propose an efficient condensation
framework that addresses these limitations by matching the feature-embedding
between synthetic set and real set. Furthermore, we enhance the diversity of
features by incorporating the strategies of early-stage augmented models and
multi-formation. Extensive experiments provide compelling evidence of the
remarkable superiority of our approach, both in terms of performance and
efficiency, compared to state-of-the-art baseline methods
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