arXiv:1907.12769
An Empirical Study of Propagation-based Methods for Video Object Segmentation
원문 초록
While propagation-based approaches have achieved state-of-the-art performance for video object segmentation, the literature lacks a fair comparison of different methods using the same settings. In this paper, we carry out an empirical study for propagation-based methods. We view these approaches from a unified perspective and conduct detailed ablation study for core methods, input cues, multi-object combination and training strategies. With careful designs, our improved end-to-end memory networks achieve a global mean of 76.1 on DAVIS 2017 val set.
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MUVOD: A Novel Multi-view Video Object Segmentation Dataset and A Benchmark for 3D Segmentation
MATNet: Motion-Attentive Transition Network for Zero-Shot Video Object Segmentation
Memory-Augmented SAM2 for Training-Free Surgical Video Segmentation
Separable Structure Modeling for Semi-supervised Video Object Segmentation
NOCTIS: Novel Object Cyclic Threshold based Instance Segmentation
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