报告题目：Pairwise Bilinear Model for Few-shot Fine-grained Image Classification
报告人：Dr. Jian Zhang, 悉尼科技大学
Deep neural networks have demonstrated advanced abilities on various visual classification tasks, which heavily rely on the large-scale training samples with annotated ground-truth. However, it is unrealistic always to require such annotation in real-world applications. Recently, Few-Shot learning (FS), as an attempt to address the shortage of training samples, has made significant progress in generic classification tasks. Nonetheless, it is still challenging for current FS models to distinguish the subtle differences between fine-grained categories given limited training data.
To filling the classification gap, in this paper, we address the Few-Shot Fine-Grained (FSFG) classification problem, which focuses on tackling the fine-grained classification under the challenging few-shot learning setting. A novel low-rank pairwise bilinear pooling operation is proposed to capture the nuanced differences between the support and query images for learning an effective distance metric. Moreover, a feature alignment layer is designed to match the support image features with query ones before the comparison. We name the proposed model Low-Rank Pairwise Alignment Bilinear Network (LRPABN), which is trained in an end-to-end fashion. Comprehensive experimental results on four widely used fine-grained classification datasets demonstrate that our LRPABN model achieves the superior performances compared to state-of-the-art methods.
Dr. Jian Zhang received the Ph.D. degree in electrical engineering from the University of New South Wales (UNSW), Sydney, Australia, in 1999.From 1997 to 2003, he was with the Visual Information Processing Laboratory, Motorola Labs, Sydney, as a Senior Research Engineer, and later became a Principal Research Engineer and a Foundation Manager with the Visual Communications Research Team. From 2004 to July 2011, he was a Principal Researcher and a Project Leader with National ICT Australia, Sydney. He is currently an Associate Professor with the Global Big Data Technologies Centre, School of Electrical & Data Engineering, Faculty of engineering and Information Technology, University of Technology Sydney, Sydney. Prof Zhang’s research interests include multimedia signal processing, computer vision, pattern recognition, visual information mining, human-computer interaction and intelligent video surveillance systems. Prof Zhang has co-authored more than 130 paper publications, book chapters, patents and technical reports from his research output, he was the co-author of eight granted US and China patents. Dr. Zhang is an IEEE Senior Member. He was Technical Program Chair, 2008 IEEE Multimedia Signal Processing Workshop; Associated Editor, IEEE Transactions on Multimedia; Associated Editor, IEEE Transactions on Circuits and Systems for Video Technology (T-CSVT); Associated Editor, EURASIP Journal on Image and Video Processing. As a General Co-Chair, Jian chaired the International Conference on Multimedia and Expo (IEEE ICME 2012) in Melbourne Australia 2012 and as a Technical Program Co-Chair for IEEE ICME 2020 in London. As a Technical Program Co-Chair, Jian chaired The IEEE Visual Communications and Image Processing (IEEE VCIP 2014) and as a General Co-Chair for organizing the IEEE VICP 2019 in Sydney.