Semantic-Assisted Object Clustering for Multi-Modal Referring Video Segmentation.

Area of Science:

• Computer Vision
• Artificial Intelligence
• Machine Learning

Background:

• Referring video segmentation requires recognizing objects specified by language cues.
• Existing methods struggle with inter-frame relationships and noisy visual data due to occlusion or motion blur.
• Vanilla attention mechanisms in temporal modeling can lead to messy representations.

Purpose of the Study:

• To develop an optimized model for multi-modal referring video segmentation.
• To enhance the utilization of inter-frame relationships and global video content.
• To address limitations of previous methods in handling noisy visual embeddings and temporal variations.

Main Methods:

• Introduced Semantic-assisted Object Cluster network (SOC) and its improved version, SOC++.
• Unified temporally selective interaction and cross-modal alignment for video-level understanding.
• Employed proxy-assisted multi-modal fusion, semantic integration with progressive frame-to-video structure, and multi-modal query contrastive supervision.
• Incorporated tendentious video aggregation by emphasizing informative frames and a dynamic query fusion module.

Main Results:

• SOC++ significantly outperforms state-of-the-art competitors on popular referring video segmentation benchmarks.
• The method demonstrates enhanced segmentation stability and adaptability, particularly for text expressions with temporal variations.
• Achieved remarkable margins in performance across all tested benchmarks.

Conclusions:

• The proposed SOC++ model effectively addresses challenges in multi-modal referring video segmentation.
• The emphasis on temporal coherence and cross-modal alignment leads to superior video-level understanding.
• The method offers a robust solution for accurate and stable object segmentation guided by language descriptions.