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Leveraging Large Language Models for Accurate Skeleton-based Action Recognition

Core Concepts
Large language models can be effectively leveraged as powerful action recognizers by projecting skeleton sequences into "action sentences" that are compatible with the models' pre-trained knowledge.
The paper proposes a novel framework called LLM-AR that treats large language models as action recognizers for skeleton-based human action recognition. The key aspects are: Linguistic Projection Process: An action-based VQ-VAE model is used to project each input skeleton sequence into a sequence of discrete tokens, forming an "action sentence". A human-inductive-biases-guided learning strategy is incorporated to make the "action sentences" more similar to human language sentences. A hyperbolic codebook is used in the VQ-VAE model to better represent the tree-like structure of human skeletons. Large Language Model Integration: The pre-trained large language model (e.g., LLaMA) is used as the action recognizer, with its pre-trained weights kept untouched. Low-rank adaptation (LoRA) is performed to enable the large language model to understand the projected "action sentences". The proposed LLM-AR framework consistently achieves state-of-the-art performance on multiple benchmark datasets for skeleton-based action recognition, including NTU RGB+D, NTU RGB+D 120, Toyota Smarthome, and UAV-Human. The framework also demonstrates promising results on recognizing actions from unseen classes, leveraging the rich pre-learned knowledge in the large language model.
The NTU RGB+D dataset contains around 56k skeleton sequences from 60 activity classes. The NTU RGB+D 120 dataset contains more than 114k skeleton sequences across 120 activity classes. The Toyota Smarthome dataset contains 16,115 video samples over 31 activity classes. The UAV-Human dataset contains more than 20k skeleton sequences over 155 activity classes.
"Motivated by this, in this work we are wondering, if we can also treat the large language model as an action recognizer in skeleton-based human action recognition?" "Taking this into account, in this work, we aim to harness the large language model as an action recognizer, and at the same time keep its pre-trained weights untouched to preserve its pre-learned rich knowledge."

Key Insights Distilled From

by Haoxuan Qu,Y... at 04-02-2024
LLMs are Good Action Recognizers

Deeper Inquiries

How can the linguistic projection process be further improved to better capture the nuances and dynamics of human actions?

To enhance the linguistic projection process for better capturing the nuances and dynamics of human actions, several improvements can be considered: Contextual Embeddings: Incorporating contextual embeddings can help capture the temporal dependencies and contextual information within the action sequences. This can provide a more comprehensive understanding of the actions being performed. Attention Mechanisms: Introducing attention mechanisms can allow the model to focus on specific parts of the action sequence that are most relevant at different time steps. This can help in capturing the key movements and gestures that define each action. Hierarchical Representation: Implementing a hierarchical representation of the action sequences can help in capturing both the fine-grained details and the overall structure of the actions. This can provide a more holistic view of the actions being recognized. Dynamic Time Warping: Incorporating dynamic time warping techniques can help align and compare actions that may vary in speed or duration. This can improve the model's ability to recognize actions with different temporal dynamics. Fine-tuning with Action-Specific Data: Fine-tuning the linguistic projection process with action-specific data can help tailor the model to recognize the unique characteristics of different actions. This personalized approach can improve the model's accuracy in action recognition.

How can the potential limitations of using large language models as action recognizers be addressed, and what are these limitations?

Large language models used as action recognizers may face several limitations, including: Lack of Specificity: Large language models may not be specialized for action recognition tasks, leading to a lack of specificity in understanding the nuances of human actions. Computational Complexity: Processing large amounts of data for action recognition tasks using language models can be computationally intensive and time-consuming. Interpretability: Large language models may lack interpretability in the context of action recognition, making it challenging to understand how decisions are made. Data Efficiency: Language models may require a large amount of labeled data for training, which can be a limitation in scenarios where labeled action data is limited. To address these limitations, several strategies can be implemented: Transfer Learning: Pre-training language models on action-specific data or fine-tuning them on action recognition tasks can improve their performance and specificity for recognizing actions. Model Distillation: Distilling large language models into smaller, more efficient models can reduce computational complexity while maintaining performance. Hybrid Models: Combining large language models with specialized action recognition models can leverage the strengths of both approaches for improved accuracy and efficiency. Interpretability Techniques: Implementing interpretability techniques such as attention mechanisms or visualization tools can help understand how language models make decisions in action recognition tasks.

How can the proposed framework be extended to leverage multimodal data (e.g., RGB videos, depth maps) for more comprehensive action recognition?

To extend the proposed framework to leverage multimodal data for more comprehensive action recognition, the following steps can be taken: Data Fusion: Integrate the linguistic projection process with features extracted from RGB videos and depth maps. This fusion of modalities can provide a more comprehensive representation of the actions. Multimodal Preprocessing: Develop preprocessing techniques to align and synchronize data from different modalities to ensure that the information from each modality complements each other. Multimodal Attention Mechanisms: Implement attention mechanisms that can dynamically focus on relevant information from different modalities at different time steps. This can enhance the model's ability to capture important features from each modality. Cross-Modal Learning: Train the model to learn correlations and dependencies between different modalities to improve action recognition accuracy. This can be achieved through joint training or cross-modal learning techniques. Fine-tuning with Multimodal Data: Fine-tune the model using a combination of unimodal and multimodal data to adapt the model to the specific characteristics of the multimodal input. By incorporating these strategies, the proposed framework can effectively leverage multimodal data to enhance the accuracy and robustness of action recognition across different modalities.