READMem: Robust Embedding Association for a Diverse Memory in Unconstrained Video Object Segmentation

• URL: http://arxiv.org/abs/2305.12823v2
• Date: Mon, 25 Sep 2023 13:36:44 GMT
• Title: READMem: Robust Embedding Association for a Diverse Memory in Unconstrained Video Object Segmentation
• Authors: St\'ephane Vujasinovi\'c, Sebastian Bullinger, Stefan Becker, Norbert Scherer-Negenborn, Michael Arens and Rainer Stiefelhagen
• Abstract summary: We present READMem, a modular framework for sVOS methods to handle unconstrained videos. We propose a robust association of the embeddings stored in the memory with query embeddings during the update process. Our approach achieves competitive results on the Long-time Video dataset (LV1) while not hindering performance on short sequences.
• Score: 24.813416082160224
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: We present READMem (Robust Embedding Association for a Diverse Memory), a modular framework for semi-automatic video object segmentation (sVOS) methods designed to handle unconstrained videos. Contemporary sVOS works typically aggregate video frames in an ever-expanding memory, demanding high hardware resources for long-term applications. To mitigate memory requirements and prevent near object duplicates (caused by information of adjacent frames), previous methods introduce a hyper-parameter that controls the frequency of frames eligible to be stored. This parameter has to be adjusted according to concrete video properties (such as rapidity of appearance changes and video length) and does not generalize well. Instead, we integrate the embedding of a new frame into the memory only if it increases the diversity of the memory content. Furthermore, we propose a robust association of the embeddings stored in the memory with query embeddings during the update process. Our approach avoids the accumulation of redundant data, allowing us in return, to restrict the memory size and prevent extreme memory demands in long videos. We extend popular sVOS baselines with READMem, which previously showed limited performance on long videos. Our approach achieves competitive results on the Long-time Video dataset (LV1) while not hindering performance on short sequences. Our code is publicly available.

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