Boosting Instance Segmentation with Pseudo Depth Maps

Conceptos Básicos

The author introduces pseudo-depth maps to improve instance segmentation performance by capturing depth differences between instances.

Resumen

The research addresses the limitations of box supervision in distinguishing foreground from background within target boxes. By incorporating pseudo-depth maps and a depth prediction layer, the network can simultaneously predict masks and depth, leading to significant improvements on datasets like Cityscapes and COCO.
The study explores the impact of pseudo-depth maps in weakly supervised instance segmentation tasks. By integrating depth features into mask predictions and utilizing depth consistency losses, the network achieves more accurate segmentations. The self-distillation process further refines the model by selecting reliable pseudo masks based on depth matching scores.
The proposed method leverages coarse pseudo-depth maps during training to enhance instance segmentation results. By fusing a depth estimation layer into the mask prediction head and incorporating a depth consistency loss, the network can better perceive instance-level depth features for improved segmentation performance.

Estadísticas

The proposed method achieves 2.7% mask AP improvement with ResNet50 on Cityscapes.
Achieved 41.0% mask AP with Swin-Base on COCO dataset.

Citas

Ideas clave extraídas de

Boosting Box-supervised Instance Segmentation with Pseudo Depth

by Xinyi Yu,Lin... a las arxiv.org 03-05-2024

https://arxiv.org/pdf/2403.01214.pdf

Consultas más profundas

How does the integration of pseudo-depth maps affect the overall accuracy of instance segmentation

The integration of pseudo-depth maps significantly enhances the accuracy of instance segmentation by providing depth-related information to the network. By incorporating depth cues into the training process, the network can better distinguish foreground from background within specified target boxes. This additional depth information helps in capturing nuanced depth differences between instances, leading to more precise and accurate segmentation results. The pseudo-depth maps guide the network in producing consistent predictions for regions with similar depth features, thereby improving overall performance.

What are potential drawbacks or limitations of relying on off-the-shelf depth predictors for generating pseudo-depth maps

One potential drawback of relying on off-the-shelf depth predictors for generating pseudo-depth maps is inaccuracies in the generated depths. Since these models are not specifically trained for a particular dataset or task, they may produce coarse or inaccurate depth estimates that could impact the quality of pseudo-depth maps. Inaccurate depth information can mislead the instance segmentation network and result in incorrect mask predictions. Additionally, off-the-shelf predictors may not capture fine-grained details required for precise instance segmentation tasks, leading to suboptimal performance.

How might leveraging depth information impact other computer vision tasks beyond instance segmentation

Leveraging depth information can have a significant impact on various computer vision tasks beyond instance segmentation. For example:

Semantic Segmentation: Depth cues can help improve semantic segmentation by providing additional context about object boundaries and spatial relationships.
Object Detection: Depth-aware features can enhance object detection algorithms by aiding in accurate localization and classification of objects based on their relative distances.
Panoptic Segmentation: Integrating depth information into panoptic segmentation networks can facilitate better understanding of scene geometry and semantics simultaneously.
Scene Understanding: Depth data enables machines to perceive 3D structure and understand spatial layouts more effectively, benefiting tasks like scene reconstruction and understanding.
Overall, leveraging depth information across different computer vision tasks has the potential to enhance model performance and enable more robust visual perception systems.