MeViS: A Comprehensive Benchmark for Video Segmentation with Motion Expressions
Introduction
MeViS, short for Motion Expressions Video Segmentation, is an innovative project designed to enhance the field of video segmentation by integrating motion expressions as guiding cues. The main goal of this project is to effectively segment objects in videos using descriptions that focus on the movement of these objects. This approach represents a significant shift from existing datasets that do not fully utilize the dynamic nature of video content in language-guided segmentation tasks.
The Motivation Behind MeViS
Traditional video segmentation datasets often overlook the importance of motion cues in guiding the segmentation process. MeViS addresses this gap by providing a robust dataset filled with motion descriptions to pinpoint target objects amidst complex settings. Such a focus not only fosters the development of more sophisticated segmentation algorithms but also encourages the exploration of language-guided video understanding.
Dataset Overview
The MeViS dataset stands out due to its large scale and complexity. It includes over 2,000 videos paired with nearly 29,000 motion-centric sentences, which help users identify target objects. Each video in the dataset offers rich details through its motion expressions, challenging segmentation models to rely on motion instead of static frames.
In comparison to other video segmentation datasets, MeViS provides a richer variety of expressions and a higher number of objects per video, significantly enhancing the depth and breadth of video content that can be explored. For example, where existing datasets might indicate the presence of an object, MeViS provides the precise motion description necessary for distinguishing that object from similar ones.
Dataset Structure
MeViS adopts a structured format that includes:
- JPEGImages: Contains the individual video frames.
- meta_expressions.json: Lists the motion expressions and metadata for each video.
- mask_dict.json: Holds the ground-truth masks for objects in COCO RLE format.
This format is akin to the Refer-YouTube-VOS dataset, aiding users already familiar with similar datasets.
Training and Evaluation
MeViS offers a meticulously designed split for training and evaluation purposes:
- Training Set: Comprises 1,662 videos with over 23,000 motion expressions.
- Validation Sets (Valᵤ and Val): These provide videos for offline evaluation and CodaLab online evaluation, helping refine algorithms before live testing.
- Test Set: Contains videos reserved for competitive evaluation.
Online Evaluation Platform
Participants can submit segmentation results via CodaLab, an online platform built to streamline the evaluation process. Prior to submission, it's recommended to test models locally using the Valᵤ set to fine-tune performance outcomes.
Installation and Inference
For those interested in implementing MeViS, detailed instructions for setting up the inference environment and conducting segmentation are provided. The segment configuration capitalizes on state-of-the-art architectures like Swin-Tiny and others, ensuring cutting-edge performance and results.
Conclusion
MeViS serves as an ambitious benchmark in the language-guided video segmentation arena, emphasizing motion expressions as key indicators for more intricate and accurate video analysis. By advancing understanding and methodologies in this domain, MeViS paves the way for future developments in intelligent video systems and multimedia understanding.
For further information and access to resources like the dataset, scripts, and evaluation protocol, please refer to the MeViS Project Page and additional documentation provided.
Citation and Licensing
Researchers leveraging MeViS in their work are encouraged to reference the dataset in their publications. MeViS is available under a CC BY-NC-SA 4.0 License, restricting its usage to non-commercial research applications only.