Source-linked AI summary
MeViS: A Large-scale Benchmark for Video Segmentation with Motion Expressions
Henghui Ding, Chang Liu, Shuting He, Xudong Jiang, Chen Change Loy
TL;DR
Existing referring video datasets often make target identification possible from static attributes, leaving motion underemphasized. The paper introduces MeViS, benchmarks existing RVOS methods, and provides LMPM as a baseline; the benchmark shows that effective motion-expression-guided segmentation remains unresolved.
Problem
Existing referring video object datasets commonly emphasize salient objects and static attributes, while motion remains underemphasized for language-guided video segmentation.
Method
The paper constructs the MeViS dataset around motion expressions and presents LMPM, which models temporal motion and matches language with predicted object trajectories.
Results
The benchmark evaluates five existing RVOS methods and finds that effective motion-expression-guided video segmentation remains unresolved.
Takeaways & Limitations
MeViS provides a foundation for developing language-guided video segmentation algorithms that use motion expressions as a primary cue in complex scenes.
Takeaways & Limitations
LMPM can lose targets when objects disappear and reappear or when multiple-object motions become complex and tangled.
Abstract
from arXiv · showhide
This paper strives for motion expressions guided video segmentation, which focuses on segmenting objects in video content based on a sentence describing the motion of the objects. Existing referring video object datasets typically focus on salient objects and use language expressions that contain excessive static attributes that could potentially enable the target object to be identified in a single frame. These datasets downplay the importance of motion in video content for language-guided video object segmentation. To investigate the feasibility of using motion expressions to ground and segment objects in videos, we propose a large-scale dataset called MeViS, which contains numerous motion expressions to indicate target objects in complex environments. We benchmarked 5 existing referring video object segmentation (RVOS) methods and conducted a comprehensive comparison on the MeViS dataset. The results show that current RVOS methods cannot effectively address motion expression-guided video segmentation. We further analyze the challenges and propose a baseline approach for the proposed MeViS dataset. The goal of our benchmark is to provide a platform that enables the development of effective language-guided video segmentation algorithms that leverage motion expressions as a primary cue for object segmentation in complex video scenes. The proposed MeViS dataset has been released at https://henghuiding.github.io/MeViS.
1. Introduction
The paper argues that existing language-guided video segmentation datasets underemphasize temporal motion and introduces MeViS to study motion-expression-guided segmentation in complex scenes.
- Existing datasets often use salient objects and static attributes observable in a single frame, giving motion properties less emphasis.
- MeViS contains 2,006 videos, 8,171 objects, and 28,570 motion expressions for referring to target objects.
- MeViS emphasizes motion by selecting coexisting moving objects, excluding isolated objects, and prioritizing expressions without static clues.
- Unlike prior single-target datasets, MeViS includes multi-object expressions that can refer to multiple target objects.
- The benchmark evaluates five existing RVOS methods and examines challenges involving fleeting movements and long-term actions across video frames.
- LMPM uses language-conditioned object queries, motion perception over object embeddings, Transformer decoding, and similarity matching to identify target trajectories.
- The dataset and baseline are intended to support future algorithms that use motion expressions as a primary segmentation cue in complex scenes.
2. Related Work
Related work frames referring video segmentation as language-guided object segmentation built on image segmentation, multimodal fusion, and temporal propagation approaches.
- Referring image segmentation grounds a target object from a natural-language expression and generates its corresponding image mask.
- Earlier referring image segmentation methods commonly combined FCN image features with RNN language features through multimodal fusion modules.
- Some image-based methods decouple the task into instance segmentation followed by language-object matching.
- Other approaches use cross-modal self-attention to select meaningful words and image pixels for contextual understanding.
- Referring video segmentation is typically treated as semi-supervised video object segmentation by replacing mask references with language references.
- Representative video methods perform frame-level segmentation and propagate masks across clips using temporal consistency or memory attention.
3. MeViS Dataset
MeViS is a large-scale motion-expression video segmentation dataset designed around complex scenes, substantial object motion, and language that cannot generally identify targets from a single frame. Its construction and statistics emphasize longer videos, more objects, multi-object references, and motion-focused expressions.
- Video Collection: MeViS selects videos with multiple objects and substantial motion, excluding scenes containing only one or two salient objects or little movement.
- Language Expression Annotation: Annotators select one or several objects and write referring expressions under rules prioritizing significant motion and excluding unnecessary static attributes.
- Language Expression Validation: Expressions are removed when a target can be identified from a single frame without motion information, and validation independently compares annotator and validator target selections.
- Dataset Statistics: 2,006 videos and 8,171 objects comprise MeViS, which focuses on segmenting objects indicated by motion expressions.
- Video Content: 13.16 seconds is MeViS's average video duration, significantly longer than Refer-Youtube-VOS, while its 4.28 objects per video also increase scene complexity.
- Target Objects and Language: 10.88 seconds is MeViS's average target-object duration, more than twice Refer-Youtube-VOS's average, and each expression refers to 1.59 objects on average.
4. Experiment
Experiments show that MeViS requires temporal motion understanding beyond static appearance cues, while LMPM provides a baseline that models global temporal context and object trajectories.
- Dataset Necessity and Challenges: 27.8% J &F: VLT struggles on MeViS despite achieving 60.4% and 63.1% J &F on DAVIS17-RVOS and Refer-Youtube-VOS, respectively.The comparison indicates that image-based methods using static clues perform well on earlier datasets but poorly on MeViS.
- Dataset Necessity and Challenges: Temporal context improves both VLT and ReferFormer by approximately 5% J &F on MeViS.The reported gain underscores the importance of temporal information for motion-expression segmentation.
- Dataset Necessity and Challenges: MeViS contains motions spanning random numbers of frames, requiring models to capture both fleeting actions and long-term movements.The dataset challenges video and language understanding because relevant motion may occur briefly or across the entire video.
- LMPM: A Simple Baseline Approach: LMPM detects language-relevant objects, represents them with object embeddings, models global temporal context, and predicts trajectories with a Transformer decoder.Its language-guided queries filter irrelevant objects before motion perception and trajectory prediction.
- LMPM: A Simple Baseline Approach: 31.0% J &F: the language-only LMPM variant using five sampled frames neglects global temporal context and cannot effectively process long-term motions.A matching mechanism for multiple target objects improves over the corresponding variant by 0.9% J &F.
- MeViS Benchmark Results: LMPM succeeds on some long-term motions but fails when targets disappear and reappear or become tangled during complex motion.These cases demonstrate the remaining need for global temporal understanding and motion-expression comprehension.
5. Conclusion and Discussion
MeViS establishes a benchmark for motion-expression-guided video segmentation, but effectively leveraging motion as the primary cue remains unresolved and requires further research.
- Conclusion: Effectively understanding and leveraging motion expressions as a primary cue for video object segmentation remains an unresolved challenge.The paper identifies this challenge as a direction for future research.
- Conclusion: MeViS provides a foundation for developing more advanced language-guided video segmentation algorithms.The benchmark is intended to support progress on motion-focused segmentation in complex video scenes.
- Future Directions: Future work includes better motion modeling, robust handling of diverse temporal spans and complex scenes, efficient object detection, cross-modal fusion, transfer learning, and open-world concepts.The listed directions span visual-language understanding, efficiency, adaptation, and broader concept coverage.