The tremendous semantic discrepancy between the line art drawings without texture and the reference pictures containing rich color challenges current image-to-image translation models. Previous works attempt to establish cross-domain correspondence. However, they fail to capture more detailed features. A Reference-based Line art Translation Network (RLTN) is introduced with a Multi-level Feature Aggregation Module (MFAM) to improve the performance. The MFAM concentrates on more meaningful information for feature matching by utilizing the Multi-stream High Frequency Block (MHFB) and the Pixel-wise Correlation Block (PCB). We also employ the Channel-level Attention Block (CAB) and the Spatial-level Attention Block (SAB) for a better fusion of features. Moreover, a Style-based Contrastive Loss (SCL) is proposed to maintain the style similarity between the synthesized images and the reference examples. Experiments conducted on three datasets demonstrate the effectiveness of our model in producing more pleasing visual effects compared with state-of-the-art approaches.

Click-through rate prediction is a critical task in online advertising. Currently, many existing methods attempt to extract user potential interests from historical click behavior sequences. However, it is difficult to handle sparse user behaviors or broaden interest exploration. Recently, some researchers incorporate the item-item co-occurrence graph as an auxiliary. Due to the elusiveness of user interests, those works still fail to determine the real motivation of user click behaviors. Besides, those works are more biased towards popular or similar commodities. They lack an effective mechanism to break the diversity restrictions. In this paper, we point out two special properties of triangles in the item-item graphs for recommendation systems: Intra-triangle homophily and Inter-triangle heterophiy. Based on this, we propose a novel and effective framework named Triangle Graph Interest Network (TGIN). For each clicked item in user behavior sequences, we introduce the triangles in its neighborhood of the item-item graphs as a supplement. TGIN regards these triangles as the basic units of user interests, which provide the clues to capture the real motivation for a user clicking an item. We characterize every click behavior by aggregating the information of several interest units to alleviate the elusive motivation problem. The attention mechanism determines users' preference for different interest units. By selecting diverse and relative triangles, \short brings in novel and serendipitous items to expand exploration opportunities of user interests. Then, we aggregate the multi-level interests of historical behavior sequences to improve CTR prediction. Extensive experiments on both of public and industrial datasets clearly verify the effectiveness of our framework.

Citing comprehensive and correct related work is crucial in academic writing. It can not only support the author's claims but also help readers trace other related research papers. Nowadays, with the rapid increase in the number of scientific literatures, it has become increasingly challenging to search for high-quality citations and write the manuscript. In this paper, we present an automatic writing assistant model, AutoCite, which not only infers potentially related work but also automatically generates the citation context at the same time. Specifically, AutoCite involves a novel multi-modal encoder and a multi-task decoder architecture. Based on the multi-modal inputs, the encoder in AutoCite learns paper representations with both citation network structure and textual contexts. The multi-task decoder in AutoCite couples and jointly learns citation prediction and context generation in a unified manner. To effectively join the encoder and decoder, we introduce a novel representation fusion component, i.e., gated neural fusion, which feeds the multi-modal representation inputs from the encoder and creates outputs for the downstream multi-task decoder adaptively. Extensive experiments on five real-world citation network datasets validate the effectiveness of our model.