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- W2771408051 abstract "Convolutional Neural Networks (CNNs) have been widely used for many computer vision tasks and produce discriminative and rich representations for images or regions of an image. Recognizing scenes requires both local object features and global semantic information as a scene image is usually composed of multiple objects which are organized with specific spatial distribution. To address these problems, in this paper, we propose a deep network architecture which models the sequential object context of scenes to capture object level information. We first detect a set of obejcts in a scene image, and then apply a pre-trained CNN to extract discriminative features for these objects. Then we use a Long Short-Term Memory (LSTM) network to get the context features by progressively receiving all contextual objects. The learned sequential object context incorporates object-object relationship and object-scene relationship in an end-to-end trainable manner. We evaluate our model on two benchmark datasets and achieve promising results compared to state-of-the-art methods." @default.
- W2771408051 created "2017-12-22" @default.
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- W2771408051 date "2017-01-01" @default.
- W2771408051 modified "2023-09-26" @default.
- W2771408051 title "Scene Recognition with Sequential Object Context" @default.
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- W2771408051 doi "https://doi.org/10.1007/978-981-10-7305-2_10" @default.
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