Scaling Up Multi-domain Semantic Segmentation by Leveraging Sentence Embeddings

Centrala begrepp

A method for merging multiple semantic segmentation datasets by replacing class labels with sentence embeddings, enabling training on a large-scale dataset and achieving state-of-the-art performance on zero-shot and supervised benchmarks.

Sammanfattning

The paper proposes a method to scale up multi-domain semantic segmentation by leveraging sentence embeddings to merge diverse datasets. The key insights are:

1. Instead of manually unifying the label taxonomies across datasets, the authors replace each class label with a sentence embedding generated by a language model (CLIP). This allows seamless merging of datasets with inconsistent label spaces.

2. By merging 9 datasets totaling over 2 million images, the authors create a large-scale training dataset. This enables their model to achieve state-of-the-art performance on 7 benchmark datasets, even without training on them directly.

3. To handle the varying annotation quality across merged datasets, the authors propose a heterogeneous loss function. This combines pixel-wise supervision for high-quality datasets, selective supervision for noisy datasets, and distillation from a CLIP model for weakly-annotated datasets.

4. The sentence embeddings enable zero-shot segmentation of unseen classes, by simply calculating the cosine similarity between the predicted embeddings and new class descriptions.

5. The robustly trained model also boosts the performance of downstream applications like depth estimation and instance segmentation, even with limited fine-tuning.

Overall, the paper presents an effective and scalable approach to multi-domain semantic segmentation, leveraging language understanding to overcome the challenges of merging diverse datasets.

Statistik

"Labeling a large-scale dataset is challenging and expensive."
"We merged publicly available noisy and weak annotations with the most finely annotated data, over 2 million images."
"Our method can segment unseen labels based on the closeness of language embeddings, showing strong generalization to unseen image domains and labels."

Citat

"Rather than attempting to manually unify a diverse set of taxonomies, and the corresponding labeled instances, here we propose a method for automatically merging datasets by replacing the labels therein."
"By merging 9 datasets, we gain access to about 2 Million training images, which span multiple domains."
"Our method not only significantly improves model performance and generalization ability to various domains, but also offers the advantage that the resulting model is able to generalize to unseen labels."

Viktiga insikter från

Scaling up Multi-domain Semantic Segmentation with Sentence Embeddings

by Wei Yin,Yifa... på arxiv.org 05-01-2024

https://arxiv.org/pdf/2202.02002.pdf

Djupare frågor

How could the proposed sentence embedding approach be extended to handle more complex label descriptions, such as multi-sentence or paragraph-level class definitions

The proposed sentence embedding approach can be extended to handle more complex label descriptions by incorporating techniques to process multi-sentence or paragraph-level class definitions. One approach could involve using natural language processing (NLP) models that are capable of understanding and encoding longer text sequences. For multi-sentence descriptions, the text could be concatenated and fed into the language model to generate a single embedding that captures the overall meaning of the class. Alternatively, for paragraph-level descriptions, the model could be designed to handle longer input sequences and generate embeddings that represent the entire paragraph.
Another strategy could involve pre-processing the complex descriptions to extract key information or features that are most relevant for semantic segmentation. This could involve techniques such as text summarization or information extraction to condense the text into a more manageable format for the language model. By focusing on the most salient information in the descriptions, the model can generate embeddings that effectively capture the essence of the class labels.

What are the potential limitations of the language model-based approach, and how could they be addressed to further improve zero-shot segmentation performance

The language model-based approach for zero-shot segmentation may have limitations related to the quality and diversity of the training data, the complexity of the label descriptions, and the generalization ability of the model. To address these limitations and further improve performance, several strategies can be considered:

Data Quality and Diversity: Ensuring a diverse and representative training dataset is crucial for the model to learn robust representations. Continuously updating and expanding the training data with new examples can help improve the model's ability to generalize to unseen domains and labels.

Label Description Complexity: Handling more complex label descriptions may require fine-tuning the language model on specific types of text data or incorporating domain-specific knowledge. Customizing the language model architecture or training procedure to better capture the nuances of the class definitions can enhance the quality of the embeddings.

Model Generalization: To improve zero-shot segmentation performance, techniques like domain adaptation or meta-learning can be employed to enhance the model's ability to transfer knowledge across different domains. By exposing the model to a wider range of data distributions and label semantics, it can learn more robust and generalizable representations.

Evaluation and Feedback Loop: Implementing a feedback loop mechanism where the model's predictions are evaluated and used to refine the training data or update the language model can help iteratively improve performance. Incorporating human feedback or active learning strategies can also enhance the model's accuracy over time.

Given the success in boosting downstream tasks like depth estimation and instance segmentation, how could the sentence embedding framework be applied to enable cross-task knowledge transfer and joint optimization across diverse computer vision problems

The sentence embedding framework can be applied to enable cross-task knowledge transfer and joint optimization across diverse computer vision problems by leveraging the shared semantic representations encoded in the language embeddings. Here are some ways this can be achieved:

Multi-Task Learning: By training a single model on multiple related tasks such as semantic segmentation, depth estimation, and instance segmentation, the model can learn to extract common features and representations that are beneficial for all tasks. The shared language embeddings can serve as a unifying factor that facilitates knowledge transfer across tasks.

Transfer Learning: Pre-training the language model on a large corpus of text data and then fine-tuning it on specific computer vision tasks can help transfer linguistic knowledge to visual understanding. The fine-tuned language embeddings can then be used to initialize models for different tasks, enabling faster convergence and improved performance.

Joint Optimization: Designing a unified framework that jointly optimizes multiple tasks using a shared feature space can enhance the overall performance. By incorporating the language embeddings as a common representation layer, the model can effectively integrate information from different tasks and improve overall efficiency and accuracy.

Task-Specific Adaptation: Tailoring the language embeddings to capture task-specific semantics and features can further enhance performance on individual tasks. By fine-tuning the embeddings or incorporating task-specific information during training, the model can adapt to the unique requirements of each task while still benefiting from the shared knowledge encoded in the embeddings.