Learning Video Object Segmentation from Static Images (1612.02646v1)

Abstract: Inspired by recent advances of deep learning in instance segmentation and object tracking, we introduce video object segmentation problem as a concept of guided instance segmentation. Our model proceeds on a per-frame basis, guided by the output of the previous frame towards the object of interest in the next frame. We demonstrate that highly accurate object segmentation in videos can be enabled by using a convnet trained with static images only. The key ingredient of our approach is a combination of offline and online learning strategies, where the former serves to produce a refined mask from the previous frame estimate and the latter allows to capture the appearance of the specific object instance. Our method can handle different types of input annotations: bounding boxes and segments, as well as incorporate multiple annotated frames, making the system suitable for diverse applications. We obtain competitive results on three different datasets, independently from the type of input annotation.

• The paper introduces guided instance segmentation to leverage previous frame masks for accurate video segmentation.
• It demonstrates that combining offline and online training with static image annotations achieves competitive mIoU across diverse datasets.
• The method offers a practical solution for video segmentation without extensive video annotations, reducing computational complexity.

Learning Video Object Segmentation from Static Images

The paper entitled "Learning Video Object Segmentation from Static Images" presents a novel approach towards solving the video object segmentation problem through guided instance segmentation. The authors propose an innovative method where a convolutional neural network (convnet), originally trained on static images, performs per-frame segmentation by leveraging output from the previous frame to guide the segmentation of the current frame. This method is distinguished by its effective utilization of both offline and online learning strategies.

Key Methodological Insights

The authors introduce guided instance segmentation as a core concept. The network is guided by the previous frame's mask to focus on the object of interest in the current frame. This process eschews the need for densely annotated video data, leveraging only static images for training. The training process involves a creative mask deformation technique to simulate the variability in object appearance across frames.

The system employs a feed-forward architecture, which allows it to produce results efficiently without needing multi-frame connections, thus remaining computationally efficient. Notably, the model adaptation happens during both offline and online phases:

1. Offline Learning: The convnet is trained using static images with annotated masks. Training data are augmented through affine transformations and non-rigid deformations of masks, providing robustness to the model against the inaccuracies typically present during test time.
2. Online Learning: Fine-tuning is conducted using the annotated mask of the initial video frame. This step specializes the model to specific objects within new video sequences, balancing generalization and specificity in instance segmentation.

Experimental Results and Observations

The methodology is validated across three heterogeneous datasets: DAVIS, YoutubeObjects, and DBLP:conf/bmvc/TsaiFR10. The results show competitive performance, achieving high mIoU scores, and demonstrate the system's robustness across various video challenges such as occlusion, fast motion, and appearance changes.

The paper includes a detailed ablation analysis, highlighting the significance of each component in the system. The impact of various forms of mask deformations, training data volume, and the introduction of optical flow as a supplementary guide are examined. It is evident that online fine-tuning contributes significantly to maintaining the model's performance over prolonged video sequences.

Conclusions and Implications

This paper charts a promising path forward in video object segmentation by demonstrating that high accuracy can be achieved using a convnet trained solely on static images. By effectively marrying offline and online approaches, and efficiently guiding segmentation through prior frame data, the presented technique offers a practical solution for scenarios where full video annotations are impractical.

For the field of AI, this work suggests potential avenues for integrating instance segmentation models without extensive requirement of annotated video data. Future work might explore incorporating temporal dimensions within network architectures to further enhance performance, as well as integrating more sophisticated global optimization techniques to refine results.

In summary, this research provides essential insights and a practical framework that can be applied to diverse applications, potentially impacting various areas in computer vision and across related fields.