XMem: Long-Term Video Object Segmentation with an Atkinson-Shiffrin Memory Model

Abstract

We present XMem, a video object segmentation architecture for long videos with unified feature memory stores inspired by the Atkinson-Shiffrin memory model. Prior work on video object segmentation typically only uses one type of feature memory. For videos longer than a minute, a single feature memory model tightly links memory consumption and accuracy. In contrast, following the Atkinson-Shiffrin model, we develop an architecture that incorporates multiple independent yet deeply-connected feature memory stores: a rapidly updated sensory memory, a high-resolution working memory, and a compact thus sustained long-term memory. Crucially, we develop a memory potentiation algorithm that routinely consolidates actively used working memory elements into the long-term memory, which avoids memory explosion and minimizes performance decay for long-term prediction. Combined with a new memory reading mechanism, XMem greatly exceeds state-of-the-art performance on long-video datasets while being on par with state-of-theart methods (that do not work on long videos) on short-video datasets. 1 1 Code is available at hkchengrex.github.io/XMem

Visual Model of Pattern Design Based on Deep Convolutional Neural Network

“…It explores and verifies the way of external scale connection to further improve the transmission quality and performance of display images. Reference [4] proposes a structural framework called multi-scale progressive fusion neural network connection to eliminate the noise formed in dual-display images. For the noise formed by the similarity caused by different nodes, the global texture effect is captured by means of recursive calculation.…”