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Fine-grained Image Classification and Retrieval by Combining Visual and Locally Pooled Textual Features
Text contained in an image carries high-level semantics that can be expl...
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Multi-Modal Retrieval using Graph Neural Networks
Most real world applications of image retrieval such as Adobe Stock, whi...
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TRACE: Transform Aggregate and Compose Visiolinguistic Representations for Image Search with Text Feedback
The ability to efficiently search for images over an indexed database is...
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Enhanced Characterness for Text Detection in the Wild
Text spotting is an interesting research problem as text may appear at a...
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Rethinking movie genre classification with fine-grained semantic clustering
Movie genre classification is an active research area in machine learnin...
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Don't only Feel Read: Using Scene text to understand advertisements
We propose a framework for automated classification of Advertisement Ima...
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Neural Network Interpretation via Fine Grained Textual Summarization
Current visualization based network interpretation methodssuffer from la...
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Multi-Modal Reasoning Graph for Scene-Text Based Fine-Grained Image Classification and Retrieval
Scene text instances found in natural images carry explicit semantic information that can provide important cues to solve a wide array of computer vision problems. In this paper, we focus on leveraging multi-modal content in the form of visual and textual cues to tackle the task of fine-grained image classification and retrieval. First, we obtain the text instances from images by employing a text reading system. Then, we combine textual features with salient image regions to exploit the complementary information carried by the two sources. Specifically, we employ a Graph Convolutional Network to perform multi-modal reasoning and obtain relationship-enhanced features by learning a common semantic space between salient objects and text found in an image. By obtaining an enhanced set of visual and textual features, the proposed model greatly outperforms the previous state-of-the-art in two different tasks, fine-grained classification and image retrieval in the Con-Text and Drink Bottle datasets.
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