Medical image classification and diagnosis is currently a hot topic in the field of deep learning. The ACM International Conference on Multimedia and Simula co-hosted the MutilMedia Grand Challenge, which aims to use artificial intelligence aiding detection and classification of gastrointestinal image. This competition is divided into four subtasks, including detection, efficient detection, efficient detection (the same hardware for all the participants) and report generation. We participate in the multi-label detection task and propose a new attention model, which can effectively improve the network's ability to classify different types of categories. Our approach also uses a series of different techniques including multi-epoch fusion, automatic data augmentation selection, and adaptive threshold selection. Combining these techniques, we are able to achieve good classification results on the given dataset. Finally, our f1 score is 0.907 and MCC is 0.952 with a high speed.

Video identification is an important task in the practical application and industry. Based on the iQIYI-VID-2019 dataset, ACM International Conference on Multimedia and iQIYI co-hosted the celebrity video identification challenge. We take part in the competition, propose a new feature fusion method and design a residual dense network which can improve video identification performance in the complex scenes. Only with face features, we achieve 0.9035 in mean Average Precision(mAP) which win the second place on the leadboard. At the same time, it is the best score only with official features. It is worth mention that the flops of our model is only 0.5G and the time required to predict the entire test dataset is only 2 sim 5 minutes. Our method takes accuracy and speed into account, which has a strong practical significance.