Title: Robust and Compact Deep Learning Features for Image Matching and Retrieval (Slides)
Abstract: Feature representations based on deep learning allow for study of feature distribution properties that are critical for robust visual matching and retrieval. Various loss functions like pairwise, triplet, or global losses have been developed recently with impressive performance. In this talk, we will present a few ideas to achieve desirable feature distribution properties (spread-pout, compact, discriminative) for large-scale applications. We will present a general regularization step to make features spread out in the feature space. We will present a temperature control mechanism to make the classifier-based features more compact and separable. Finally, we will tackle the large-scale indexing problem - how to preserve the logarithmic search ability of the recent emergent small-world graph indexing scheme when reducing features to compact binary hash code. (Based on joint work with Xu Zhang and Svebor Karaman).
Biography: Prof. Shih-Fu Chang is the Richard Dicker Professor at Columbia University, with appointments in both Electrical Engineering Department and Computer Science Department. His research is focused on multimedia information retrieval, computer vision, machine learning, and signal processing. A primary goal of his work is to develop intelligent systems that can extract rich information from the vast amount of visual data such as those emerging on the Web, collected through pervasive sensing, or available in gigantic archives. His work on content-based visual search in the early 90's, VisualSEEk and VideoQ, set the foundation of this vibrant area. Over the years, he continued to develop innovative solutions for image/video recognition, multimodal analysis, visual content ontology, image authentication, and compact hashing for large-scale indexing. His scholarly work can be seen in more than 300 peer-reviewed publications, many best paper awards, more than 30 issued patents, and technologies licensed to seven companies. He was listed as the Most Influential Scholar in the field of Multimedia by Aminer in 2016. For his long-term pioneering contributions, he has been awarded the IEEE Signal Processing Society Technical Achievement Award, ACM Multimedia Special Interest Group Technical Achievement Award, Honorary Doctorate from the University of Amsterdam, the IEEE Kiyo Tomiyasu Award, and IBM Faculty Award. He served as Chair of ACM SIGMM (2013-2017), Chair of Columbia Electrical Engineering Department (2007-2010), the Editor-in-Chief of the IEEE Signal Processing Magazine (2006-8), and advisor for several international research institutions and companies. He is a Fellow of the American Association for the Advancement of Science (AAAS), ACM, and IEEE.