Contrastive Learning for Fine-grained Visual Recognition

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F49777513%3A23520%2F23%3A43969940" target="_blank" >RIV/49777513:23520/23:43969940 - isvavai.cz</a>
Result on the web
<a href="http://svk.fav.zcu.cz/download/proceedings_svk_2023.pdf" target="_blank" >http://svk.fav.zcu.cz/download/proceedings_svk_2023.pdf</a>
DOI - Digital Object Identifier
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Result language
angličtina
Original language name
Contrastive Learning for Fine-grained Visual Recognition
Original language description
Contrastive learning is a type of representation learning which retains a representation by comparing the input samples, e.g., images, video, text, and sound. Having good representation can be beneficial for the interpretability of Deep Neural Networks (DNNs) and for some downstream tasks like open-set recognition. Contrastive learning compares positive pairs of similar inputs and negative pairs of dissimilar inputs. The key component is the contrastive loss which measures the similarity between feature vectors and enforces minimization and maximization of the similarity between positive and negative pairs. Modern contrastive learning methods are often applied in self-supervised settings, while discriminative cross-entropy learning is widely used in supervised settings. In this work, we employ supervised contrastive learning to fine-tune DNNs for fine-grained recognition.
Czech name
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Czech description
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Publication year
2023
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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