Semantic Object Segmentation in Tagged Videos via Detection.

Area of Science:

• Computer Vision
• Machine Learning
• Artificial Intelligence

Background:

• Semantic Object Segmentation (SOS) is crucial for understanding visual data.
• Supervised models excel in image-based SOS but struggle with weakly annotated videos.
• Video-based SOS faces challenges due to limited detailed annotations.

Purpose of the Study:

• To develop a robust framework for semantic object segmentation in videos with weak tag annotations.
• To address the limitations of directly training supervised models on tagged videos.
• To improve the accuracy and spatiotemporal consistency of object segmentation in videos.

Main Methods:

• A segmentation-by-detection framework utilizing pre-trained object detection and segment proposal models.
• An efficient algorithm to initialize object tracks via a joint assignment problem.
• A voting-based refinement algorithm to enhance spatiotemporal consistency of object tracks.

Main Results:

• The proposed framework effectively segments semantic objects in tagged videos.
• Robust performance demonstrated even with inaccurate initial proposals from image-based detectors.
• Substantial improvements over state-of-the-art methods on public benchmarks.

Conclusions:

• The novel framework offers a viable solution for semantic object segmentation in challenging weakly annotated video data.
• The approach enhances spatiotemporal consistency, leading to more accurate object segmentation.
• This work advances the capabilities of computer vision in video understanding.