报告题目：Image Retrieval with Deep Learning
报 告 人：周文罡副教授（中国科学技术大学）
报告摘要：Recent years has witnessed the great success of deep learning in a variety of vision tasks. In most cases, deep learning is conducted in a supervised way. As for image search, since the category number of potential objects is difficult to enumerate and the image database is large, it is infeasible to collect sufficient annotated training images as supervision for deep learning. As a result, most works on image search simply leverage the activations from pre-trained deep learning model, or just focus on some specific fine-grained tasks, such as landmark retrieval. To this end, we explore deep learning in a pseudo-supervised paradigm and orient it for image retrieval. We approach it from different perspectives and propose three algorithms. Further, to automatically evaluate the retrieval result quality, we propose a deep learning based quality assessment method. Extensive experiments demonstrate the effectiveness and potential of pseudo-supervised deep learning in retrieval task.