MISS: Memory-efficient Instance Segmentation Framework By Visual Inductive Priors Flow Propagation

• URL: http://arxiv.org/abs/2403.11576v1
• Date: Mon, 18 Mar 2024 08:52:23 GMT
• Title: MISS: Memory-efficient Instance Segmentation Framework By Visual Inductive Priors Flow Propagation
• Authors: Chih-Chung Hsu, Chia-Ming Lee,
• Abstract summary: The strategic integration of a visual prior into the training dataset emerges as a potential solution to enhance congruity with the testing data distribution. Our empirical evaluations underscore the efficacy of MISS, demonstrating commendable performance in scenarios characterized by limited data availability and memory constraints.
• Score: 8.727456619750983
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Instance segmentation, a cornerstone task in computer vision, has wide-ranging applications in diverse industries. The advent of deep learning and artificial intelligence has underscored the criticality of training effective models, particularly in data-scarce scenarios - a concern that resonates in both academic and industrial circles. A significant impediment in this domain is the resource-intensive nature of procuring high-quality, annotated data for instance segmentation, a hurdle that amplifies the challenge of developing robust models under resource constraints. In this context, the strategic integration of a visual prior into the training dataset emerges as a potential solution to enhance congruity with the testing data distribution, consequently reducing the dependency on computational resources and the need for highly complex models. However, effectively embedding a visual prior into the learning process remains a complex endeavor. Addressing this challenge, we introduce the MISS (Memory-efficient Instance Segmentation System) framework. MISS leverages visual inductive prior flow propagation, integrating intrinsic prior knowledge from the Synergy-basketball dataset at various stages: data preprocessing, augmentation, training, and inference. Our empirical evaluations underscore the efficacy of MISS, demonstrating commendable performance in scenarios characterized by limited data availability and memory constraints.

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