Reducing Annotation Cost for Video Instance Segmentation with Point Supervision

Core Concepts

This work introduces a point-supervised video instance segmentation framework that can achieve competitive performance compared to fully-supervised methods, by leveraging class-agnostic proposal generation and a spatio-temporal point-based matcher to generate high-quality dense pseudo-labels from sparse point annotations.

Abstract

The paper addresses the problem of video instance segmentation (VIS), which aims to detect, segment, and track objects in videos. Conventional VIS methods rely on densely-annotated object masks, which are expensive to obtain. The authors propose a point-supervised VIS framework (PointVIS) that can achieve competitive performance compared to fully-supervised methods, while significantly reducing the annotation cost.
The key components of PointVIS are:

Class-agnostic proposal generation: The authors leverage a pre-trained image instance segmentation model to generate class-agnostic spatio-temporal instance proposals for each video, without requiring video-based training.
Spatio-temporal point-based matcher: The authors design a matching cost function that combines cues from both point annotations and the spatio-temporal proposals to effectively match proposals and video objects with points.
Self-training for generalization: The authors conduct self-training to mitigate the domain gap between images and videos and refine the results.

Comprehensive experiments on three VIS benchmarks (YouTube-VIS 2019, YouTube-VIS 2021, and OVIS) demonstrate the effectiveness of PointVIS. With only one point per object, PointVIS can achieve 87.5%, 83.7%, and 72.6% of the performance of fully-supervised methods on the respective datasets. The authors also conduct extensive ablation studies to analyze the impact of different components and point selection strategies.

Stats

With one single point per object, PointVIS achieves 53.9 mAP on YouTube-VIS 2019, which is 87.5% of the performance of the fully-supervised counterpart.
With one positive and one negative point per object, PointVIS achieves 59.6 mAP on YouTube-VIS 2019, which is 96.7% of the fully-supervised counterpart.
On YouTube-VIS 2021, PointVIS achieves 87.7% of the performance of the fully-supervised counterpart.
On OVIS, PointVIS achieves 72.6% of the performance of the fully-supervised counterpart.

Quotes

"PointVIS is the first attempt to comprehensively investigate video instance segmentation with point-level supervision. Our work significantly reduces the amount of required annotations in VIS and opens up the possibility to address the task with minimal supervision."
"Even one positive point annotated per video object already achieves good performance, retaining 87% of the performance of fully-supervised methods on Youtube-VIS 2019."
"Given positive points, increasing negative points improves performance, while adding positive points alone could provide little gain."

Key Insights Distilled From

What is Point Supervision Worth in Video Instance Segmentation?

by Shuaiyi Huan... at arxiv.org 04-03-2024

https://arxiv.org/pdf/2404.01990.pdf

Deeper Inquiries

How can the proposed point-supervised framework be extended to other video understanding tasks, such as video object detection or video panoptic segmentation

The proposed point-supervised framework can be extended to other video understanding tasks by adapting the point-based matching and pseudo-label generation strategies to suit the specific requirements of tasks like video object detection or video panoptic segmentation.
For video object detection, the framework can be modified to generate bounding boxes around objects based on the annotated points. By incorporating object tracking techniques, the framework can track objects across frames and generate detections for each object instance. The point annotations can serve as the starting point for generating object proposals, which can then be refined and classified to perform object detection in videos.
In the case of video panoptic segmentation, the framework can be enhanced to handle both semantic segmentation and instance segmentation simultaneously. By leveraging the point annotations to generate instance masks and incorporating semantic segmentation information, the framework can assign a semantic label to each pixel in the video frames, distinguishing between stuff and things classes. This approach would require a more sophisticated matching algorithm to ensure accurate segmentation results for both stuff and things categories.
Overall, by customizing the point-supervised framework for specific video understanding tasks, it can be adapted to address a wide range of challenges in video analysis and achieve high performance in tasks like video object detection and video panoptic segmentation.

What are the potential challenges in applying the point-supervised approach to real-world scenarios with a large number of object categories and complex occlusions

Applying the point-supervised approach to real-world scenarios with a large number of object categories and complex occlusions may pose several challenges:

Annotation Quality: Ensuring the accuracy and consistency of point annotations for a diverse set of object categories can be challenging. Annotating points for a large number of categories requires careful labeling to capture the unique characteristics of each object instance accurately.

Handling Occlusions: Complex occlusions in real-world scenarios can make it difficult to generate accurate pseudo-masks based on point annotations. Objects may be partially or fully occluded, leading to challenges in matching proposals with annotated points and generating precise instance masks.

Generalization to New Categories: Adapting the point-supervised approach to handle new or unseen object categories requires robust mechanisms for incorporating novel classes into the training process. Ensuring that the model can generalize effectively to diverse categories is crucial for real-world applications.

Scalability: Scaling the point-supervised framework to handle a large number of object categories and complex scenes requires efficient algorithms for point-based matching and pseudo-label generation. Managing the computational complexity and memory requirements for processing extensive video data is essential for practical deployment.

Addressing these challenges will be crucial for the successful application of the point-supervised approach to real-world scenarios with diverse object categories and complex occlusions.

Can the point-based matching and pseudo-label generation strategies be further improved to handle more diverse video data and achieve even higher performance compared to fully-supervised methods

The point-based matching and pseudo-label generation strategies can be further improved to handle more diverse video data and achieve higher performance compared to fully-supervised methods by incorporating the following enhancements:

Multi-Point Matching: Instead of relying on a single positive point per object, incorporating multiple positive points and refining the matching algorithm to consider the spatial relationships between these points can improve the accuracy of pseudo-label generation.

Semantic Context: Integrating semantic context information into the matching process can help refine the pseudo-labels by considering the surrounding context of the annotated points. This can enhance the quality of the generated masks and improve the overall segmentation performance.

Temporal Consistency: Enhancing the matching algorithm to ensure temporal consistency across video frames can improve the tracking and segmentation accuracy. By considering the temporal evolution of object instances, the model can generate more coherent and consistent pseudo-labels.

Adaptive Sampling: Implementing adaptive sampling strategies for selecting positive and negative points based on the complexity of the scene and the object categories can improve the robustness of the framework. Dynamic sampling techniques can help address challenges such as occlusions and object variations.

By incorporating these improvements, the point-based matching and pseudo-label generation strategies can be optimized to handle diverse video data more effectively and achieve higher performance levels, closing the gap with fully-supervised methods in video instance segmentation tasks.