[CVPR 2023] Breakthrough in Semantic Segmentation using Natural Language

[CVPR 2023] Breakthrough in Semantic Segmentation using Natural Language

Table of Contents:

1. Introduction
2. Traditional Semantic Segmentation 2.1 Training and Testing Sets 2.2 Limitations in Real-World Scenarios
3. Open Vocabulary Semantic Segmentation 3.1 Training on Image Caption Data 3.2 Zero Shot Transfer
4. Previous Works on Open Vocabulary Semantic Segmentation 4.1 Leveraging Pre-trained Clip Model 4.2 Importance of Extensive Image Caption Pre-training
5. Introducing the Monarch Architecture 5.1 Visual Grouping 5.2 Group Text Alignment
6. Proxy Tasks for Learning Visual Environments 6.1 Mask Entity Completion 6.2 Cross-Image Mass Consistency
7. Data Efficiency in Training 7.1 Filtering Noisy Image Caption Dataset 7.2 Commonly Appeared Entities
8. Zero Shot Transfer Results 8.1 Comparison with Fine-tuning Model 8.2 Comparison with Clip Model
9. Impact of Mask Entity Completion Loss 9.1 Improved Visual Grouping and Text Matching 9.2 Comparison with Variants
10. Future Directions for Model Improvement 10.1 Handling Background Classes 10.2 Applying in Visual Grouping Framework
11. Conclusion
12. Availability of Code and Data

Introduction

In this article, we will explore the concept of learning open vocabulary semantic segmentation models from natural language supervision. Traditional semantic segmentation tasks have limitations as they are restricted to a closed set of predefined categories. To overcome these limitations, we focus on open vocabulary semantic segmentation, where the model is trained on image caption data and can then transfer to any segmentation dataset in a zero-shot manner.

Traditional Semantic Segmentation

Traditionally, semantic segmentation models are trained and tested on datasets that share the same object categories. This means that the model is limited to the predefined categories and cannot segment Novel categories, which is not practical in real-world scenarios.

Open Vocabulary Semantic Segmentation

Open vocabulary semantic segmentation allows the model to be trained on image caption data and transfer to any segmentation dataset. This approach enables the model to segment novel categories and improves its practicality in real-world scenarios.

Previous Works on Open Vocabulary Semantic Segmentation

Some previous works have leveraged pre-trained Clip models for open vocabulary semantic segmentation. These models have adopted image caption data to train vision transformers with zero transfer ability. However, extensive image caption pre-training is required for these models.

Introducing the Monarch Architecture

The Monarch architecture is introduced as a solution for open vocabulary semantic segmentation. This architecture consists of two steps: visual grouping and group text alignment. The visual encoder incorporates a slot Attention-Based binding module in a naive Vision Transformer to dynamically cluster image tokens into different groups.

Proxy Tasks for Learning Visual Environments

To learn the visual environments, two proxy tasks are introduced. The first task is mask entity completion, where the model completes mask entities by querying the visual groups. The Second task is cross-image mass consistency, which ensures consistent mask predictions between images with shared entities.

Data Efficiency in Training

Training on web-collected image caption data is challenging as the Captions only provide coarse image-level descriptions. To enable data-efficient pre-training, noisy image caption datasets are filtered by keeping only those that contain commonly appeared entities.

Zero Shot Transfer Results

The model, trained with zero-shot transfer, outperforms models that require full fine-tuning on VOC datasets. It also achieves comparable results to the concurrent work Clip.py, which uses a larger in-house dataset for training.

Impact of Mask Entity Completion Loss

The mask entity completion loss improves both visual grouping and group text matching. It surpasses variants such as single entry masking or masking language modeling. The model achieves better segmentation results with the help of mask entity completion and cross-image mass consistency losses.

Future Directions for Model Improvement

There are potential future directions to improve the model's performance. These include better handling of background classes, which are usually not Mentioned in the captions. The segmentation editing model can also be applied in a framework to improve visual grouping.

Conclusion

In conclusion, learning open vocabulary semantic segmentation models from natural language supervision allows the model to segment novel categories and transfer to any segmentation dataset. The Monarch architecture, with its proxy tasks and data efficiency techniques, achieves impressive results in zero-shot transfer scenarios.

Availability of Code and Data

The code and data used in this work are publicly available for further exploration and development.

Learning Open Vocabulary Semantic Segmentation Models from Natural Language Supervision

Semantic segmentation refers to the task of classifying and segmenting objects within an image. Traditionally, semantic segmentation models are trained and tested on datasets that share the same set of object categories. This means that the model is limited to a closed set of predefined categories and cannot segment novel categories, making it impractical in real-world scenarios.

To address this limitation, researchers have been exploring the concept of open vocabulary semantic segmentation. This approach involves training the model on image caption data and then transferring it to any segmentation dataset in a zero-shot manner. This means that the model can segment novel categories without the need for fine-tuning or additional training.

Previous works in this field have leveraged pre-trained Clip models for open vocabulary semantic segmentation. These models adopt image caption data to train vision transformers with zero transfer ability. However, extensive image caption pre-training is required for these models, making them inefficient and time-consuming.

In this work, the researchers introduce the Monarch architecture, which overcomes the limitations of previous models and supports mask-free training and zero-shot transfer. The architecture consists of two main steps: visual grouping and group text alignment. In the visual grouping step, the model takes the image and a set of learnable group tokens as input. The image encoder incorporates a slot attention-based binding module in a naive Vision Transformer. This module dynamically clusters the image tokens into different groups. In the group text alignment step, the average group tokens are aligned to the sentence embedding of the raw caption via contrastive learning.

To learn the visual environments, the researchers introduce two proxy tasks: mask entity completion and cross-image mass consistency. The mask entity completion task involves the text encoder taking a mask sentence, where all the entities in the sentence are replaced with a special mask token. The model then uses a lightweight Transformer decoder to complete the mask entities by querying the visual groups. The completed embedding is aligned to a prompted entity embedding, which serves as the answer to the mask caption. The cross-image mass consistency task ensures consistent mask predictions between images that contain shared entities. The model computes a pairwise dice loss over two sets of masks obtained from different images that have been matched based on the concept of interest.

The researchers also address the challenge of training on web-collected image caption data, which provides only coarse image-level descriptions. To enable data-efficient pre-training, the researchers filter out noisy image caption datasets by keeping only those that contain commonly appeared entities. They discard non-visual entities such as view or illustration masks, as they do not correspond to any visual objects in an image.

The results of the model trained with zero-shot transfer are impressive. It outperforms models that require full fine-tuning on popular visual object classes (VOC) datasets. In fact, even compared to the concurrent work Clip.py, which uses a larger in-house dataset for training, the model achieves comparable results using only three percent of the data. The mask entity completion loss plays a crucial role in improving both visual grouping and group text matching. It surpasses variants such as single entry masking or masking language modeling. The model achieves better segmentation results with the help of the mask entity completion and cross-image mass consistency losses.

In conclusion, learning open vocabulary semantic segmentation models from natural language supervision opens up new possibilities for segmenting novel categories and transferring the model to any segmentation dataset without the need for fine-tuning. The Monarch architecture, with its innovative proxy tasks and data efficiency techniques, demonstrates the potential of this approach. Future directions for model improvement include better handling of background classes and applying the segmentation editing model in a framework to improve visual grouping.

Highlights:

• Open vocabulary semantic segmentation allows segmentation of novel categories without predefined categories.
• The Monarch architecture supports mask-free training and zero-shot transfer.
• Proxy tasks of mask entity completion and cross-image mass consistency improve the model's performance.
• Data-efficient pre-training filters out noisy image caption datasets and focuses on commonly appeared entities.
• The model achieves impressive segmentation results and outperforms fine-tuning models and concurrent works.

FAQ:

Q: What is semantic segmentation? A: Semantic segmentation refers to the task of classifying and segmenting objects within an image.

Q: What are the limitations of traditional semantic segmentation? A: Traditional semantic segmentation models are limited to a closed set of predefined categories and cannot segment novel categories.

Q: What is open vocabulary semantic segmentation? A: Open vocabulary semantic segmentation allows models to segment novel categories by training on image caption data and transferring to any segmentation dataset.

Q: How does the Monarch architecture improve open vocabulary semantic segmentation? A: The Monarch architecture supports mask-free training and zero-shot transfer, improving the practicality and efficiency of the model.

Q: How does the mask entity completion task contribute to the model's performance? A: The mask entity completion task improves visual grouping and group text matching, leading to better segmentation results.

Q: How does the model achieve data efficiency in training? A: The model filters noisy image caption datasets and focuses on commonly appeared entities for data-efficient pre-training.

Q: Does the model outperform other fine-tuning models and concurrent works? A: Yes, the model achieves impressive segmentation results and outperforms fine-tuning models and concurrent works, even with limited pre-training data.