Enhancing Action Recognition by Leveraging the Hierarchical Structure of Actions and Textual Context (2410.21275v1)

Abstract: The sequential execution of actions and their hierarchical structure consisting of different levels of abstraction, provide features that remain unexplored in the task of action recognition. In this study, we present a novel approach to improve action recognition by exploiting the hierarchical organization of actions and by incorporating contextualized textual information, including location and prior actions to reflect the sequential context. To achieve this goal, we introduce a novel transformer architecture tailored for action recognition that utilizes both visual and textual features. Visual features are obtained from RGB and optical flow data, while text embeddings represent contextual information. Furthermore, we define a joint loss function to simultaneously train the model for both coarse and fine-grained action recognition, thereby exploiting the hierarchical nature of actions. To demonstrate the effectiveness of our method, we extend the Toyota Smarthome Untrimmed (TSU) dataset to introduce action hierarchies, introducing the Hierarchical TSU dataset. We also conduct an ablation study to assess the impact of different methods for integrating contextual and hierarchical data on action recognition performance. Results show that the proposed approach outperforms pre-trained SOTA methods when trained with the same hyperparameters. Moreover, they also show a 17.12% improvement in top-1 accuracy over the equivalent fine-grained RGB version when using ground-truth contextual information, and a 5.33% improvement when contextual information is obtained from actual predictions.

• The paper presents a novel transformer that integrates visual and textual features to capture both coarse- and fine-grained action details.
• It employs a joint loss function to effectively leverage hierarchical action structures, achieving a 17.12% top-1 accuracy boost with ground-truth context.
• Rigorous experiments on the Hierarchical TSU dataset demonstrate significant improvements over SOTA benchmarks in diverse video analytics applications.

Enhancing Action Recognition by Leveraging the Hierarchical Structure of Actions and Textual Context

This paper introduces a novel method aimed at enhancing action recognition by integrating hierarchical action structures with contextualized textual data. Specifically, it explores the potentials of leveraging the hierarchical organization of actions coupled with textual context information to improve action recognition performance, utilizing a tailored transformer architecture. Such an initiative aligns with ongoing efforts in the field of computer vision, particularly concerning the analysis and interpretation of human activities in video sequences.

The research highlights the sequential nature and hierarchical abstraction of actions—elements that have been underutilized in existing action recognition methodologies but hold substantial promise for improvement. To tap into these opportunities, the authors propose employing a novel vision-language transformer that seamlessly integrates both visual and textual features. Visual representations are extracted from RGB and optical flow data, while textual embeddings encode situational context, including locale and antecedent actions to reflect contextual sequences.

One of the standout contributions of this work is the definition of a joint loss function that simultaneously trains the model for coarse- and fine-grained action recognition. This dual objective function is strategically exploited to take advantage of the hierarchical nature inherent in action structures. By extending the Toyota Smarthome Untrimmed (TSU) dataset, renamed as Hierarchical TSU dataset, the efficacy of the proposed methodology is demonstrated through rigorous experimentation, including ablation studies, which parse the impact of distinct methods for incorporating contextual and hierarchical information on action recognition.

Critically, the experimental outcomes show that this proposed approach significantly outperforms existing pre-trained state-of-the-art (SOTA) benchmarks when the models are trained with identical hyperparameters. For instance, the results demonstrate a remarkable 17.12% increase in top-1 accuracy over conventional fine-grained RGB versions when ground-truth contextual information is utilized, alongside a 5.33% escalation when such context is derived from actual predictions. These numerical results attest to the augmentation in recognition accuracy afforded by fusing hierarchical structures and context data.

The theoretical ramifications extend to demonstrating the inherent capabilities of transformers to effectively model both hierarchical and temporal dependencies in video data, indicating a robust avenue for integrating multimodal data streams. Practically, the proposed framework presents clear applicability in complex real-world scenarios, from autonomous navigation systems to intelligent monitoring solutions in diverse settings, where distinguishing between subtle nuances of actions is crucial.

Speculation on future developments involving AI suggests that continued exploration into vision-language transformers will further hone mechanisms for incorporating deeper contextual awareness within action recognition tasks. Such advancements could catalyze more nuanced, contextually rich models that offer superior performance across varied application domains, solidifying the role of hierarchical and contextual knowledge for video analytics in AI-driven applications. Overall, this research presents a pertinent pivot in refining how context and structure are harnessed within action recognition frameworks, paving the way for enhanced system intelligence and understanding.