Actor-agnostic Multi-label Action Recognition with Multi-modal Query (2307.10763v3)

Abstract: Existing action recognition methods are typically actor-specific due to the intrinsic topological and apparent differences among the actors. This requires actor-specific pose estimation (e.g., humans vs. animals), leading to cumbersome model design complexity and high maintenance costs. Moreover, they often focus on learning the visual modality alone and single-label classification whilst neglecting other available information sources (e.g., class name text) and the concurrent occurrence of multiple actions. To overcome these limitations, we propose a new approach called 'actor-agnostic multi-modal multi-label action recognition,' which offers a unified solution for various types of actors, including humans and animals. We further formulate a novel Multi-modal Semantic Query Network (MSQNet) model in a transformer-based object detection framework (e.g., DETR), characterized by leveraging visual and textual modalities to represent the action classes better. The elimination of actor-specific model designs is a key advantage, as it removes the need for actor pose estimation altogether. Extensive experiments on five publicly available benchmarks show that our MSQNet consistently outperforms the prior arts of actor-specific alternatives on human and animal single- and multi-label action recognition tasks by up to 50%. Code is made available at https://github.com/mondalanindya/MSQNet.

• The paper introduces MSQNet, a novel transformer-based model that eliminates the need for actor-specific design in multi-label action recognition.
• It integrates visual and textual modalities to better capture complex, co-occurring actions in video data.
• Experimental results demonstrate up to a 50% performance improvement over traditional actor-specific methods across five diverse benchmarks.

An Analysis of "Actor-agnostic Multi-label Action Recognition with Multi-modal Query"

The paper introduces an innovative approach to action recognition by proposing a framework that fundamentally diverges from traditional actor-specific models, termed as 'actor-agnostic multi-modal multi-label action recognition'. This method integrates multi-modal queries within the action recognition process, effectively leveraging both visual and textual modalities to facilitate a more comprehensive understanding of action classes.

Summary of Contributions

The authors introduce a novel Multi-modal Semantic Query Network (MSQNet), which operates on a transformer-based object detection framework such as DETR. The primary focus of MSQNet is to eliminate the reliance on actor-specific designs in action recognition, which often necessitate complex and costly pose estimation mechanisms tailored to the specific traits of different actors, be it humans or animals. Instead, MSQNet employs a unified model that is versatile in handling diverse actors and actions, thereby providing a significant advantage in reducing computational complexity and maintenance burden.

Key to MSQNet's architecture is its ability to combine visual information from video sequences and textual descriptions of action classes, enhancing the transformer network's capacity to recognize multi-label actions in videos where multiple actions may co-occur. This actor-agnostic approach also notably harmonizes various modalities — primarily visual cues and semantic textual embeddings — to furnish a deeper and richer understanding of action contexts.

Experimental Insights

Extensive experimental evaluations demonstrated that MSQNet substantially outperforms existing actor-specific models. The analysis was conducted across five different benchmarks which comprised both human and animal datasets. MSQNet showed improvements in action recognition performance by up to 50%, underlining its potential in broadening the applicability of action recognition systems beyond existing boundaries. Through rigorous benchmarking, the authors established the viability of the MSQNet framework in both single- and multi-label action recognition tasks, thereby illustrating its scalability and effectiveness in handling complex action sequences.

Implications and Future Directions

The implications of this research are manifold, particularly in enhancing the practical applications of action recognition systems across various domains such as surveillance, healthcare, and wildlife monitoring. By removing actor specificity, the MSQNet model paves the way for systems that are robust, adaptive, and easier to deploy across different environments and settings.

From a theoretical standpoint, this work prompts a reevaluation of traditional action recognition paradigms by demonstrating the efficacy of multi-modal integration and actor-agnostic frameworks. Future research can explore further integrations with other modalities such as audio signals or explore the potential for real-time applications where rapid action recognition is critical.

In conclusion, the paper represents a significant stride in refining the methodologies of action recognition, emphasizing a shift towards generalized models that maintain high performance across diverse actor categories and action types. This work demonstrates the promising capabilities of adopting advanced transformer architectures infused with multi-modal data, setting a precedent for future advancements in artificial intelligence-driven video analysis and beyond.