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Combating Human Trafficking with Multimodal Deep Models

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Figure

Figure 1:Distribution of advertisements inTrafficking-10k dataset across United States andCanada.
Figure 2: Distribution of the length of advertise-ments in Trafficking-10k. There is no significantdifference between positive and negative casespurely based on length.
Figure 3: Overview of our proposed Human Trafficking Deep Network (HTDN). The input to HTDN is textand a set of 5 images
Figure 4: 2D t-SNE representation of different input features for baseline models. Clockwise from top left:one hot vectors with expert data, one hot vectors without expert data, visual features from Vision NetworkFv, and average word vectors
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