One-Shot Open-Set Skeleton-Based Action Recognition (2209.04288v1)

Abstract: Action recognition is a fundamental capability for humanoid robots to interact and cooperate with humans. This application requires the action recognition system to be designed so that new actions can be easily added, while unknown actions are identified and ignored. In recent years, deep-learning approaches represented the principal solution to the Action Recognition problem. However, most models often require a large dataset of manually-labeled samples. In this work we target One-Shot deep-learning models, because they can deal with just a single instance for class. Unfortunately, One-Shot models assume that, at inference time, the action to recognize falls into the support set and they fail when the action lies outside the support set. Few-Shot Open-Set Recognition (FSOSR) solutions attempt to address that flaw, but current solutions consider only static images and not sequences of images. Static images remain insufficient to discriminate actions such as sitting-down and standing-up. In this paper we propose a novel model that addresses the FSOSR problem with a One-Shot model that is augmented with a discriminator that rejects unknown actions. This model is useful for applications in humanoid robotics, because it allows to easily add new classes and determine whether an input sequence is among the ones that are known to the system. We show how to train the whole model in an end-to-end fashion and we perform quantitative and qualitative analyses. Finally, we provide real-world examples.

• The paper introduces a novel FSOSAR model that integrates one-shot learning with open-set recognition for 3D skeleton data.
• The model reduces computational load by focusing on pose-specific information rather than intensive image processing.
• The paper’s discriminator effectively distinguishes known from unknown actions, enhancing reliability in dynamic human-robot interactions.

One-Shot Open-Set Skeleton-Based Action Recognition

This paper introduces an advanced approach to Few-Shot Open-Set Action Recognition (FSOSAR) specifically applied to sequences of 3D skeleton data. The presented model addresses a key challenge in action recognition for humanoid robotics: extending the recognition system to new actions while correctly identifying and disregarding unknown actions. The authors propose a system that combines the flexibility of One-Shot learning with the robustness of a discriminator that can reject unfamiliar action sequences.

Key Contributions and Findings

1. New Solution to FSOSAR: The authors develop a model tailored to the FSOSAR problem that combines Few-Shot Learning with Open-Set Recognition. Unlike previous systems limited to still images, this approach handles sequences of skeletal movements, enhancing recognition capabilities for actions that involve sequences such as sitting down or standing up.
2. Innovative Use of 3D Skeleton Data: By employing sequences of 3D skeleton data, the model bypasses the need for computationally intensive image processing and allows focus on pose-specific recognition. This approach minimizes the computational footprint while enhancing real-time applicability in robotic systems.
3. Discriminator for Open-Set Learning: The proposed architecture includes a novel discriminator that effectively distinguishes between known and unknown action sequences. This component evaluates the confidence of action classification and can "reject" uncertain predictions, thus increasing reliability in dynamic environments.
4. End-to-End Training Technique: The model is trained using a novel end-to-end approach that balances training samples for both known and unknown actions, ensuring the discriminator is effectively calibrated over time.
5. Quantitative and Qualitative Analysis: Through extensive experimental validation, the authors demonstrate the model's superior performance compared to baseline methods, particularly in accurately identifying and rejecting unknown actions.

Implications and Future Directions

The development of this model has significant implications for the design of humanoid robots that interact with humans in unstructured environments. It enables robots to learn new actions quickly and adapt to unexpected changes in human behavior, thus opening avenues for more personalized and adaptable robotic assistants.

From a theoretical standpoint, the methods introduced could influence future research on few-shot and open-set learning, particularly in domains requiring rapid adaptation to novel conditions.

As AI continues to evolve, this work suggests several potential future developments. Further refinement of the discriminator could lead to even more nuanced and granular action recognition. Additionally, integrating complementary modalities such as audio or tactile data could enhance the model's multi-modal recognition capabilities.

Overall, the proposed system represents a meaningful advancement in the ongoing effort to create intelligent robots that seamlessly integrate into human-centric environments. The method's adaptability and robust performance positions it as a promising tool for a variety of applications involving human-robot interaction.