Bridging the Semantic Gap in Action Understanding: A Structured Approach to Unify Diverse Datasets

Основні поняття

The core message of this article is that a structured semantic space based on linguistic knowledge can bridge the semantic gap across diverse action datasets, enabling more effective and transferable action understanding models.

Анотація

The article addresses the "isolated islands" problem in action understanding, where existing datasets have incompatible semantic spaces due to ambiguous and inconsistent class definitions. To address this, the authors propose a structured semantic space based on the VerbNet linguistic hierarchy, which provides unambiguous verb node definitions, rich semantic and geometric knowledge, and hierarchical structure.
The key highlights are:

The authors design a structured semantic space using the VerbNet verb taxonomy, which covers over 5,800 verbs and provides clear semantic and geometric information for each node.
They build a unified "Pangea" database by aligning the classes of 28 diverse action datasets (image, video, skeleton, MoCap) to the structured semantic space.
They propose a Physical-to-Semantic (P2S) mapping model that effectively leverages the structured semantic space for action understanding, showing significant transfer learning capabilities.
Extensive experiments demonstrate the superiority of the P2S model, especially in few-shot and zero-shot settings, compared to baselines like CLIP.
The authors also show how the structured semantic space can enable semantic-to-physical generation of actions.

Overall, the work presents a principled approach to address the long-standing challenge of semantic gaps in action understanding, paving the way for more generalizable and transferable action learning models.

Статистика

The Pangea database contains 19.5M images, 1.1M videos, and 840K 3D human samples across 28 diverse datasets.
The structured semantic space covers 513 verb nodes out of the 898 nodes in the VerbNet hierarchy, including 290 fine-grained leaf nodes.

Цитати

"We argue that we need a more principled semantic space to concentrate the community efforts and use all datasets together to pursue generalizable action learning."
"Our space has four-fold superiority: (1) Unambiguous verb nodes correlating all related verbs, (2) Rich knowledge, (3) Hierarchy to represent actions from abstract to specific granularity, (4) Extensive coverage."

Ключові висновки, отримані з

From Isolated Islands to Pangea

by Yong-Lu Li,X... о arxiv.org 04-04-2024

https://arxiv.org/pdf/2304.00553.pdf

Глибші Запити

How can the structured semantic space be further extended to cover more diverse and emerging actions?

The structured semantic space can be extended to cover more diverse and emerging actions by incorporating additional sources of semantic information. One approach could be to integrate knowledge from other linguistic resources such as WordNet, FrameNet, or other verb taxonomies to enrich the semantic space with a broader range of action classes. By leveraging these resources, the structured semantic space can capture a more comprehensive set of actions and their semantic relationships. Additionally, incorporating domain-specific knowledge or expert annotations can help in expanding the semantic space to include specialized or niche actions that may not be covered in existing datasets. Furthermore, continuous updates and revisions to the semantic space based on new data and emerging trends in action understanding can ensure its relevance and coverage of the latest actions.

What are the potential challenges in scaling up the P2S mapping model to handle larger-scale datasets and more complex physical representations?

Scaling up the P2S mapping model to handle larger-scale datasets and more complex physical representations may pose several challenges. One challenge is the computational complexity and resource requirements associated with processing and analyzing vast amounts of data. As the dataset size increases, the model may require more computational power and memory to train and infer accurately. Additionally, handling more complex physical representations, such as high-dimensional feature vectors or multi-modal inputs, can increase the model's complexity and training time. Ensuring the scalability and efficiency of the model architecture to accommodate larger datasets and diverse physical representations is crucial.
Another challenge is maintaining the interpretability and generalizability of the model as it scales up. As the model becomes more complex, it may become harder to interpret the learned representations and make sense of the mapping between physical and semantic spaces. Ensuring that the model retains its ability to generalize to new, unseen data while handling the increased complexity of larger datasets is essential. Regular model evaluation, validation, and optimization strategies are necessary to address these challenges and ensure the scalability and effectiveness of the P2S mapping model.

How can the insights from this work be applied to other domains beyond action understanding, such as object recognition or scene understanding, where semantic gaps are also prevalent?

The insights from this work can be applied to other domains beyond action understanding, such as object recognition or scene understanding, where semantic gaps are prevalent. By leveraging a structured semantic space and a mapping model like P2S, similar approaches can be adopted to bridge semantic gaps and enhance understanding in these domains.
For object recognition, a structured semantic space based on object taxonomies or hierarchies can be designed to capture the relationships between different object categories. The mapping model can then be used to align physical features of objects with their corresponding semantic representations, improving object recognition accuracy and generalization.
In scene understanding, a structured semantic space that defines relationships between scene elements, activities, and objects can help in capturing the complex interactions within a scene. By applying a mapping model similar to P2S, scene understanding systems can better interpret and analyze the components of a scene, leading to more comprehensive and accurate scene understanding.
Overall, the principles of structured semantic spaces and mapping models can be adapted and extended to various domains to address semantic gaps, improve understanding, and enhance the performance of AI systems in diverse applications beyond action understanding.