Context Propagation from Proposals for Semantic Video Object Segmentation
This work addresses the problem of accurately segmenting objects in videos for computer vision applications, representing an incremental improvement over existing methods.
The paper tackles semantic video object segmentation by learning and propagating semantic contexts from video object proposals to resolve visual ambiguities, achieving improved robustness compared to state-of-the-art methods.
In this paper, we propose a novel approach to learning semantic contextual relationships in videos for semantic object segmentation. Our algorithm derives the semantic contexts from video object proposals which encode the key evolution of objects and the relationship among objects over the spatio-temporal domain. This semantic contexts are propagated across the video to estimate the pairwise contexts between all pairs of local superpixels which are integrated into a conditional random field in the form of pairwise potentials and infers the per-superpixel semantic labels. The experiments demonstrate that our contexts learning and propagation model effectively improves the robustness of resolving visual ambiguities in semantic video object segmentation compared with the state-of-the-art methods.