Deep Image Features for Instance-level Recognition and Matching

In this talk, I will discuss recent work from our team at Google Research, covering novel methods and datasets. Instance-level recognition, retrieval and matching are key computer vision problems which generally depend on effective image representations, both global and local. Our team has proposed a suite of state-of-the-art models to address these tasks: DELF (ICCV'17), one of the first deep learning methods for joint detection & description of local image features; Detect-to-Retrieve (CVPR'19), where deep local features can be efficiently aggregated guided by a trained object detector; DELG (ECCV'20), the first end-to-end trained deep model for joint local and global feature extraction. I will also present our team's efforts on pushing for larger scale and more realistic benchmarks in this area, with the Google Landmarks Dataset (CVPR'20), and three workshops at computer vision conferences (CVPR'18, CVPR'19, ECCV'20).