Enhancing Referring Video Object Segmentation by Leveraging Long and Short Text Expressions

Core Concepts

The core message of this work is to enhance referring video object segmentation (RVOS) by leveraging both long and short text expressions. The authors propose a long-short text joint prediction network (LoSh) that utilizes a long-short cross-attention module and a long-short predictions intersection loss to better align the linguistic and visual information. Additionally, a forward-backward visual consistency loss is introduced to exploit temporal consistency in the visual features.

Abstract

The paper addresses the problem of referring video object segmentation (RVOS), which aims to segment the target instance referred by a given text expression in a video clip. The authors observe that existing RVOS models often favor more on the action- and relation-related visual attributes of the instance, leading to partial or incorrect mask prediction.

To tackle this issue, the authors propose the LoSh framework, which takes both long and short text expressions as input. The short text expression retains only the appearance-related information of the target instance, allowing the model to focus on the instance's appearance. LoSh introduces a long-short cross-attention module to strengthen the feature corresponding to the long text expression via that from the short one. Additionally, a long-short predictions intersection loss is introduced to regulate the model predictions for the long and short text expressions.

Beyond the linguistic aspect, the authors also target to improve the model on the visual part by exploiting temporal consistency over visual features. A forward-backward visual consistency loss is introduced to compute optical flows between video frames and use them to warp the features of every annotated frame's temporal neighbors to the annotated frame for consistency optimization.

The proposed LoSh is built upon two state-of-the-art RVOS pipelines, MTTR and SgMg. Extensive experiments on A2D-Sentences, Refer-YouTube-VOS, JHMDB-Sentences and Refer-DAVIS17 datasets show that LoSh significantly outperforms the baselines across all metrics.

Stats

The text expression for the target instance normally contains a sophisticated description of the instance's appearance, action, and relation with others.
Over 70% of the failure predictions by MTTR on A2D-Sentences either mis-align with appearance-related phrases or overly concentrate on the discriminative regions corresponding to actions or relations.

Quotes

"The text expression normally contains sophisticated description of the instance's appearance, action, and relation with others. It is therefore rather difficult for a RVOS model to capture all these attributes correspondingly in the video; in fact, the model often favours more on the action- and relation-related visual attributes of the instance."
"We tackle this problem by taking a subject-centric short text expression from the original long text expression. The short one retains only the appearance-related information of the target instance so that we can use it to focus the model's attention on the instance's appearance."

Key Insights Distilled From

LoSh

by Linfeng Yuan... at arxiv.org 04-03-2024

https://arxiv.org/pdf/2306.08736.pdf

Deeper Inquiries

How can the proposed LoSh framework be extended to handle more complex linguistic expressions, such as those involving multiple instances or more sophisticated relationships

The LoSh framework can be extended to handle more complex linguistic expressions by incorporating mechanisms to deal with multiple instances or sophisticated relationships. One approach could be to enhance the linguistic encoder to better capture and differentiate between different instances mentioned in the text. This could involve incorporating entity recognition techniques to identify and track multiple instances referred to in the text. Additionally, the model could be designed to handle more complex relationships by incorporating relational reasoning modules that can understand and represent intricate connections between objects in the video. By enhancing the transformer architecture to handle multiple instances and complex relationships, the LoSh framework can be adapted to address a wider range of linguistic expressions in RVOS tasks.

What are the potential limitations of the forward-backward visual consistency loss, and how could it be further improved to better capture temporal dynamics in the video

The forward-backward visual consistency loss, while effective in capturing temporal dynamics in the video, may have limitations in scenarios where there are rapid or complex movements between frames. To improve this aspect, one approach could be to incorporate more advanced optical flow estimation techniques that can better handle challenging motion patterns. Additionally, introducing a mechanism to adaptively adjust the importance of the forward and backward consistency losses based on the motion complexity in the video could enhance the model's ability to capture temporal dynamics accurately. Furthermore, integrating recurrent neural networks or temporal convolutional networks to model long-range dependencies in the video frames could also improve the model's temporal consistency capabilities.

Given the success of LoSh on RVOS, how could the ideas of leveraging long and short text expressions and exploiting temporal consistency be applied to other video understanding tasks, such as video captioning or video question answering

The success of LoSh in RVOS tasks by leveraging long and short text expressions and exploiting temporal consistency can be applied to other video understanding tasks such as video captioning or video question answering. For video captioning, the model could utilize the long text expression to generate detailed descriptions of the video content, while the short text expression could provide a concise summary or key information to guide the caption generation process. By incorporating the forward-backward visual consistency loss, the model can ensure that the generated captions are coherent and consistent with the visual content across frames. Similarly, in video question answering tasks, the long and short text expressions can be used to provide context and focus for answering questions about the video content. The temporal consistency mechanisms can help in understanding the temporal context of the video and generating accurate responses to questions based on the visual information. By adapting the principles of LoSh to these tasks, the models can achieve better performance and understanding of video content.