Non-parametric Contextual Relationship Learning for Semantic Video Object Segmentation
<|reference_start|>Non-parametric Contextual Relationship Learning for Semantic Video Object Segmentation: We propose a novel approach for modeling semantic contextual relationships in videos. This graph-based model enables the learning and propagation of higher-level spatial-temporal contexts to facilitate the semantic labeling of local regions. We introduce an exemplar-based nonparametric view of contextual cues, where the inherent relationships implied by object hypotheses are encoded on a similarity graph of regions. Contextual relationships learning and propagation are performed to estimate the pairwise contexts between all pairs of unlabeled local regions. Our algorithm integrates the learned contexts into a Conditional Random Field (CRF) in the form of pairwise potentials and infers the per-region semantic labels. We evaluate our approach on the challenging YouTube-Objects dataset which shows that the proposed contextual relationship model outperforms the state-of-the-art methods.<|reference_end|>
@article{wang2024non-parametric,
    title={Non-parametric Contextual Relationship Learning for Semantic Video
  Object Segmentation},
    author={Tinghuai Wang, Huiling Wang},
    journal={arXiv preprint arXiv:2407.05916},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.05916},
    primaryClass={cs.CV}
}