One-Shot Video Object Segmentation (1611.05198v4)

Abstract: This paper tackles the task of semi-supervised video object segmentation, i.e., the separation of an object from the background in a video, given the mask of the first frame. We present One-Shot Video Object Segmentation (OSVOS), based on a fully-convolutional neural network architecture that is able to successively transfer generic semantic information, learned on ImageNet, to the task of foreground segmentation, and finally to learning the appearance of a single annotated object of the test sequence (hence one-shot). Although all frames are processed independently, the results are temporally coherent and stable. We perform experiments on two annotated video segmentation databases, which show that OSVOS is fast and improves the state of the art by a significant margin (79.8% vs 68.0%).

• The paper presents OSVOS, which adapts a pre-trained CNN via fine-tuning to achieve one-shot video object segmentation from a single annotated frame.
• It processes each frame independently, effectively handling occlusions and abrupt motions without enforcing strict temporal consistency.
• Experimental results highlight a trade-off between speed and accuracy, achieving up to 86.9% accuracy with additional annotations and flexible fine-tuning.

Overview of OSVOS: A Semi-Supervised Approach to Video Object Segmentation

The paper investigates the problem of semi-supervised video object segmentation, specifically when only one labeled training example, such as the first frame, is available. The authors introduce a novel CNN architecture named OSVOS (One-Shot Video Object Segmentation), which is designed to address the task of segmenting all pixels in a video sequence into background and foreground.

Key Contributions

1. Adapting CNN to Specific Object Instances: The primary innovation is the adaptation of a pre-trained CNN to a specific object instance provided through a single annotated image. Initially, a generic CNN pre-trained on image recognition tasks (ImageNet) is fine-tuned on manually segmented objects from video datasets. At test time, further fine-tuning is performed on the specific object annotated in one frame. This adaptation leverages multiple levels of information, from generic semantics to the unique properties of the object of interest.
2. Frame-Independent Processing: Unlike traditional methods that enforce temporal consistency across video frames, OSVOS segments each frame independently. This approach allows handling of cases with occlusions and abrupt motion changes without relying on temporal continuity. Temporal consistency is achieved as a by-product due to the high accuracy of the deep learning model, not as an enforced constraint.
3. Balancing Speed and Accuracy: The framework allows trade-offs between computational speed and accuracy. Annotating additional frames for fine-tuning results in progressively improved segmentation accuracy. Experimental results demonstrate rapid processing capability (181 ms per frame) with an accuracy of 71.5%, which can be enhanced to 79.7% with a processing time of 7.83 seconds per frame. With the annotation of more frames, accuracy improves significantly, reaching 86.9% with four annotated frames per sequence.

Methodology

The architecture of OSVOS adopts Fully Convolutional Networks (FCNs) due to their notable performance in dense prediction tasks. FCNs, despite being constrained by the coarse scale of deeper layers, employ strategies such as skip connections and learnable filters to enhance upscaling and localization accuracy. The paper marks the first use of FCNs explicitly for video segmentation tasks.

Experimental Validation

Experiments were conducted on the DAVIS and Youtube-Objects datasets. OSVOS achieved substantial improvements over the state-of-the-art, with a reported accuracy of 79.8% compared to the previous best of 68.0%. The technique's ability to process frames at a rate of 102 ms for a typical DAVIS frame (480×854 pixels) underscores its efficiency. Enhanced supervision through additional annotated frames further boosts accuracy, underscoring the method's flexibility and potential for application in rapid rotoscoping.

Implications and Future Directions

This semi-supervised approach to video object segmentation represents a significant advancement for tasks demanding minimal manual input while maintaining high accuracy. The independence from temporal constraints allows better handling of complex scenarios such as occlusions and erratic motions. Future developments could explore further optimization of the fine-tuning process and enhancement of the basic model to boost initial accuracy, potentially reducing the need for additional annotations.

The release of OSVOS resources, including training and testing code, pre-computed results, and pre-trained models, paves the way for further research and development. This openness will undoubtedly stimulate advancement in the field of video object segmentation, fostering innovation and improving practical applications like video surveillance and automated video editing.

In sum, OSVOS provides a flexible, highly accurate, and efficient framework for video object segmentation, with promising implications for both theoretical exploration and practical deployment in AI-driven video analysis.