In this paper, we address the text and image matching in crossmodal retrieval of the fashion industry. Different from the matching in the general domain, the fashion matching is required to pay much more aention to the fine-grained information in the fashion images and texts. Pioneer approaches detect the region of interests (i.e., RoIs) from images and use the RoI embeddings as image representations. In general, RoIs tend to represent the "object-level" information in the fashion images, while fashion texts are prone to describe more detailed information, e.g. styles, aributes. RoIs are thus not fine-grained enough for fashion text and image matching. To this end, we propose FashionBERT, which leverages patches as image features. With the pre-trained BERT model as the backbone network, FashionBERT learns high level representations of texts and images. Meanwhile, we propose an adaptive loss to trade off multitask learning in the FashionBERT modeling. Two tasks (i.e., text and image matching and cross-modal retrieval) are incorporated to evaluate FashionBERT. On the public dataset, experiments demonstrate FashionBERT achieves significant improvements in performances than the baseline and state-ofthe-art approaches. In practice, FashionBERT is applied in a concrete cross-modal retrieval application. We provide the detailed matching performance and inference efficiency analysis.

No abstract

With the tremendous amount of research publications, it has become increasingly important to provide a researcher with a rapid and accurate recommendation of a list of reference papers about a research field or topic. In this paper, we propose a unified graph model that can easily incorporate various types of useful information (e.g., content, authorship, citation and collaboration networks etc.) for efficient recommendation. The proposed model not only allows to thoroughly explore how these types of information can be better combined, but also makes personalized query-oriented reference paper recommendation possible, which as far as we know is a new issue that has not been explicitly addressed in the past. The experiments have demonstrated the clear advantages of personalized recommendation over non-personalized recommendation.

No abstract

The growth of the Web 2.0 technologies has led to an explosion of social networking media sites. Among them, Twitter is the most popular service by far due to its ease for realtime sharing of information. It collects millions of tweets per day and monitors what people are talking about in the trending topics updated timely. Then the question is how users can understand a topic in a short time when they are frustrated with the overwhelming and unorganized tweets. In this paper, this problem is approached by sequential summarization which aims to produce a sequential summary, i.e., a series of chronologically ordered short subsummaries that collectively provide a full story about topic development. Both the number and the content of sub-summaries are automatically identified by the proposed stream-based and semantic-based approaches. These approaches are evaluated in terms of sequence coverage, sequence novelty and sequence correlation and the effectiveness of their combination is demonstrated.