With the development of the World Wide Web, there exists more and more illicit drug Webpages. Thus, how to screen cannabis Webpages on the internet is a quite important issue. Conventional methods that only use the keyword-based or imagebased approaches are not sufficient. We propose a Multi-Modal Multiple-Instance Learning (MMMIL) approach combining both text and image information for cannabis webpage recognition. The main technical contributions of our work are two-fold. First, the text

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... ation associated with images is used to build a preclassifier, which can pre-select pseudo positive training bags from new Webpages to update multi-modal classifier. This can be seen as a pseudo active learning process. Second, we design an efficient instance selection technique by utilizing text information to speed up the training process without compromising the performance. The experiments on a dataset containing over 40,000 images for more than 4,000 Webpages demonstrate the effectiveness and efficiency of the proposed approach.