Modular Interactive Video Object Segmentation: Interaction-to-Mask, Propagation and Difference-Aware Fusion (2103.07941v3)

Abstract: We present Modular interactive VOS (MiVOS) framework which decouples interaction-to-mask and mask propagation, allowing for higher generalizability and better performance. Trained separately, the interaction module converts user interactions to an object mask, which is then temporally propagated by our propagation module using a novel top-$k$ filtering strategy in reading the space-time memory. To effectively take the user's intent into account, a novel difference-aware module is proposed to learn how to properly fuse the masks before and after each interaction, which are aligned with the target frames by employing the space-time memory. We evaluate our method both qualitatively and quantitatively with different forms of user interactions (e.g., scribbles, clicks) on DAVIS to show that our method outperforms current state-of-the-art algorithms while requiring fewer frame interactions, with the additional advantage in generalizing to different types of user interactions. We contribute a large-scale synthetic VOS dataset with pixel-accurate segmentation of 4.8M frames to accompany our source codes to facilitate future research.

• The paper introduces MiVOS, which decouples interaction-to-mask from propagation to enhance segmentation efficiency.
• It employs a novel difference-aware fusion module and top‑k filtering to accurately align user corrections with temporal mask propagation.
• Evaluation on the DAVIS dataset shows that MiVOS outperforms state-of-the-art methods while reducing the need for extensive user interactions.

Modular Interactive Video Object Segmentation: Interaction-to-Mask, Propagation and Difference-Aware Fusion

The paper "Modular Interactive Video Object Segmentation: Interaction-to-Mask, Propagation and Difference-Aware Fusion" presents a novel framework for interactive video object segmentation (iVOS). This framework, referred to as MiVOS, strategically decouples the interaction-to-mask phase from mask propagation, enhancing generalizability and efficiency in video object segmentation tasks. The proposed MiVOS framework introduces key innovations, including a novel difference-aware fusion module and a top-$k$ filtering mechanism that enriches memory read operations during mask propagation.

Interaction-to-Mask and Propagation Modules

In the MiVOS approach, video object segmentation is addressed through a modular design, facilitating independent optimization of each module. The interaction-to-mask module is designed to process user inputs such as clicks or scribbles, converting them into object masks. These masks serve as inputs for the propagation module, which utilizes a space-time memory network inspired by previous VOS methodologies. The memory network incorporates a top-$k$ filtering strategy aimed at optimizing the spatio-temporal memory reading by focusing on the most relevant memory entries, which enhances both computational efficiency and performance stability over long video sequences.

Difference-Aware Fusion Module

A significant advancement introduced in this paper is the difference-aware fusion module, which addresses the challenge of integrating user interactions across multiple rounds with temporal mask propagation. Traditional approaches often overlook nuanced user intents during mask interactions, potentially diluting correction information between propagated frames. To mitigate this, the difference-aware fusion module employs strategies to capture and align mask differences that directly convey user correction intentions. These differences are computed as positive and negative changes in mask states before and after interactions, utilizing attention mechanisms to align them accurately with target frames. This alignment significantly contributes to maintaining intended segmentation corrections, improving overall accuracy, and reducing required user interactions.

Evaluation and Contributions

Quantitative and qualitative evaluations on the DAVIS dataset reveal that the MiVOS framework outperforms current state-of-the-art iVOS algorithms while necessitating fewer user interactions for frame corrections. A noteworthy contribution of this paper is the introduction of a synthetic VOS dataset containing 4.8 million frames with highly accurate pixel-level annotations, developed using Blender and leveraging ShapeNet models. This dataset offers substantial utility for training and benchmarking future VOS models.

Theoretically, the modular design of MiVOS presents distinct advantages in interactive video editing applications, offering enhanced user control and interaction efficiency. Practically, the reduction in user interaction time coupled with improved segmentation accuracy presents substantial implications for real-world video processing tasks.

Future Prospects

This paper lays critical groundwork for future research into interactive video object segmentation frameworks with modular designs. The proposed decoupling strategy enables seamless integration of various interaction modules, enhancing adaptability to diverse user input types. Subsequently, improvements in real-time feedback loops, interaction diversity, and propagation methodologies could lead to further refinement of interactive segmentation systems. As AI models evolve, integrating more adaptive learning mechanisms within these frameworks could enhance responsiveness and precision in user-guided video object segmentation scenarios.

In summary, the MiVOS framework represents a significant contribution to the domain of interactive video object segmentation, demonstrating promising directions for improving user experience and segmentation accuracy in time-sensitive video editing and analysis applications.