Hierarchical Memory Matching Network for Video Object Segmentation

• URL: http://arxiv.org/abs/2109.11404v1
• Date: Thu, 23 Sep 2021 14:36:43 GMT
• Title: Hierarchical Memory Matching Network for Video Object Segmentation
• Authors: Hongje Seong, Seoung Wug Oh, Joon-Young Lee, Seongwon Lee, Suhyeon Lee, Euntai Kim
• Abstract summary: We propose two advanced memory read modules that enable us to perform memory in multiple scales while exploiting temporal smoothness. We first propose a guided memory matching module that replaces the non-local dense memory read, commonly adopted in previous memory-based methods. We introduce a hierarchical memory matching scheme and propose a top-k guided memory matching module in which memory read on a fine-scale is guided by that on a coarse-scale.
• Score: 38.24999776705497
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We present Hierarchical Memory Matching Network (HMMN) for semi-supervised video object segmentation. Based on a recent memory-based method [33], we propose two advanced memory read modules that enable us to perform memory reading in multiple scales while exploiting temporal smoothness. We first propose a kernel guided memory matching module that replaces the non-local dense memory read, commonly adopted in previous memory-based methods. The module imposes the temporal smoothness constraint in the memory read, leading to accurate memory retrieval. More importantly, we introduce a hierarchical memory matching scheme and propose a top-k guided memory matching module in which memory read on a fine-scale is guided by that on a coarse-scale. With the module, we perform memory read in multiple scales efficiently and leverage both high-level semantic and low-level fine-grained memory features to predict detailed object masks. Our network achieves state-of-the-art performance on the validation sets of DAVIS 2016/2017 (90.8% and 84.7%) and YouTube-VOS 2018/2019 (82.6% and 82.5%), and test-dev set of DAVIS 2017 (78.6%). The source code and model are available online: https://github.com/Hongje/HMMN.

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