Core Concepts
The proposed Hybrid memory for Temporally consistent Referring video object segmentation (HTR) paradigm explicitly models temporal instance consistency alongside referring segmentation, achieving top-ranked performance on benchmark datasets.
Abstract
The content presents an end-to-end paradigm called HTR for referring video object segmentation (R-VOS) that achieves temporally consistent and accurate segmentation.
Key highlights:
- HTR introduces a novel hybrid memory that combines local and global representations to facilitate robust spatio-temporal propagation, even with imperfect automatically generated reference masks.
- HTR performs selective referring segmentation to generate high-quality reference masks, and then propagates these features to segment the remaining frames using the hybrid memory.
- HTR outperforms state-of-the-art R-VOS methods on popular benchmarks Ref-YouTube-VOS, Ref-DAVIS17, A2D-Sentences, and JHMDB-Sentences, achieving top-ranked performance.
- The authors propose a new Mask Consistency Score (MCS) metric to evaluate the temporal consistency of video segmentation, which shows significant improvements for HTR.
- Extensive experiments demonstrate the effectiveness of HTR's end-to-end architecture and hybrid memory in enhancing temporal consistency and segmentation quality.
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