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[2201.13182] Learning Super-Features for Image Retrieval
Source: arxiv.org
Article summary:
1. A novel architecture for deep image retrieval is proposed, based on mid-level features called Super-features.
2. These Super-features are constructed by an iterative attention module and constitute an ordered set of localized and discriminant image patterns.
3. Experiments on common landmark retrieval benchmarks show that Super-features substantially outperform state-of-the-art methods while requiring a smaller memory footprint to match their performance.
Article analysis:
The article is generally reliable and trustworthy, as it provides evidence for its claims in the form of experiments conducted on common landmark retrieval benchmarks. The article also provides a link to the code and models used in the experiments, which adds to its credibility. Furthermore, the authors provide a detailed description of their proposed architecture, which allows readers to understand how it works and why it is effective.
However, there are some potential biases in the article that should be noted. For example, the authors do not explore any counterarguments or alternative approaches to deep image retrieval that may be more effective than their proposed method. Additionally, they do not discuss any possible risks associated with using their method or any potential drawbacks that could arise from its use. Finally, the authors do not present both sides of the argument equally; instead they focus solely on promoting their own approach without considering other options or perspectives.