Video Object Segmentation with Language Referring Expressions (1803.08006v3)

Abstract: Most state-of-the-art semi-supervised video object segmentation methods rely on a pixel-accurate mask of a target object provided for the first frame of a video. However, obtaining a detailed segmentation mask is expensive and time-consuming. In this work we explore an alternative way of identifying a target object, namely by employing language referring expressions. Besides being a more practical and natural way of pointing out a target object, using language specifications can help to avoid drift as well as make the system more robust to complex dynamics and appearance variations. Leveraging recent advances of language grounding models designed for images, we propose an approach to extend them to video data, ensuring temporally coherent predictions. To evaluate our method we augment the popular video object segmentation benchmarks, DAVIS'16 and DAVIS'17 with language descriptions of target objects. We show that our language-supervised approach performs on par with the methods which have access to a pixel-level mask of the target object on DAVIS'16 and is competitive to methods using scribbles on the challenging DAVIS'17 dataset.

• The paper introduces a two-step framework that uses language referring expressions to generate bounding boxes and refine them for pixel-level segmentation.
• It employs temporal consistency to maintain spatial coherence across video frames, addressing instability in natural language grounding models.
• Experimental results on DAVIS benchmarks show that the language-guided method achieves mIoU scores comparable to traditional mask-based approaches while reducing annotation effort.

Examination of Video Object Segmentation using Language Referring Expressions

The paper addresses a pivotal task within the computer vision field: video object segmentation (VOS). The authors conceptualize an innovative approach that leverages language referring expressions as a mechanism to identify and segment target objects in videos, contrasting the traditional reliance on pixel-accurate segmentation masks from the first frame. This approach represents a practical advance due to its efficiency and potential applicability in natural human-computer interaction scenarios.

This research proposes capitalizing on natural language descriptions of objects to improve the state-of-the-art in semi-supervised video object segmentation. By employing language referring expressions, the authors aim to bypass the costly and time-intensive process of acquiring detailed initial segmentation masks, thus enhancing the system's robustness against complex dynamics and appearance variations while avoiding drift. The strategy builds upon recent advancements in language grounding models, traditionally designed for static images, extending them to handle video data with temporal coherence.

Methodology Overview

The authors present a two-step framework involving grounding and segmentation. In the first step, object bounding boxes within each video frame are generated based on provided natural language queries using models such as DBNet and MattNet. Given the inherent instability of these models when applied to consecutive video frames, a temporal consistency mechanism is introduced to enforce spatial coherence across frames.

The second step involves refining the bounding boxes to achieve pixel-level segmentation using a convolutional neural network. By integrating both appearance and motion cues, this approach enhances the segmentation performance across various video frames, even when dealing with static and moving objects.

Experimental Evaluation

The authors augment the standard VOS benchmarks, $\text{DAVIS}_{\text{16}$ and $\text{DAVIS}_{\text{17}$, with language specifications of target objects to evaluate their proposed method. The results demonstrate that the developed approach performs comparably to existing methods that utilize pixel-level masks. Notably, their language-referring methodology shows a promising capability to maintain segmentation accuracy on par with traditional methods while significantly minimizing human annotation effort. For example, on $\text{DAVIS}_{\text{16}$, the language-guided segmentation achieves mIoU scores close to those obtained via mask annotations.

Additionally, the paper presents a compelling discussion regarding the enhancement of VOS through the amalgamation of language and visual supervision, highlighting their complementarity. This combination yields superior results and presents a promising direction for further research.

Contributions and Implications

The key contributions of this paper lie in the successful expansion of language grounding from images to videos and the demonstration of language referring expressions as a viable alternative input for VOS tasks. The integration of linguistic cues is notably robust across challenging conditions such as occlusions and dynamic backgrounds, thus supporting the conjecture that language descriptions provide a beneficial complement to traditional visual methods.

The paper speculates that extending these findings could further bridge the interaction between natural language processing and vision, fostering the development of more intuitive systems for practical applications, such as video editing and augmented reality.

Future Work and Considerations

As with any cutting-edge research, there are potential areas for future exploration. The scalability of the proposed system to various types of videos with diverse domains remains an open question. Moreover, refining the grounding models to enhance their stability and accuracy across temporally diverse sequences could prove invaluable. Additionally, exploring alternative methods for generating more refined initial proposals could further bolster segmentation outcomes. The paper sets a foundation for further exploration in the intersection of linguistics and visual segmentation, opening avenues for better integrating human inputs into computer vision systems.