End-to-End Human Object Interaction Detection with HOI Transformer (2103.04503v1)

Abstract: We propose HOI Transformer to tackle human object interaction (HOI) detection in an end-to-end manner. Current approaches either decouple HOI task into separated stages of object detection and interaction classification or introduce surrogate interaction problem. In contrast, our method, named HOI Transformer, streamlines the HOI pipeline by eliminating the need for many hand-designed components. HOI Transformer reasons about the relations of objects and humans from global image context and directly predicts HOI instances in parallel. A quintuple matching loss is introduced to force HOI predictions in a unified way. Our method is conceptually much simpler and demonstrates improved accuracy. Without bells and whistles, HOI Transformer achieves $26.61\% $ $ AP $ on HICO-DET and $52.9\%$ $AP_{role}$ on V-COCO, surpassing previous methods with the advantage of being much simpler. We hope our approach will serve as a simple and effective alternative for HOI tasks. Code is available at https://github.com/bbepoch/HoiTransformer .

• The paper introduces a novel transformer-based framework that directly predicts human-object interactions from the global image context, eliminating intermediate stages.
• It leverages a quintuple matching loss and self-attention to capture long-range dependencies and contextual information in a unified end-to-end model.
• Experimental results on HICO-DET and V-COCO benchmarks show improved accuracy and efficiency compared to traditional two-stage detection methods.

End-to-End Human Object Interaction Detection with HOI Transformer

The paper, "End-to-End Human Object Interaction Detection with HOI Transformer" presents a novel approach to human-object interaction (HOI) detection by introducing a methodology that eliminates the traditionally required intermediate stages like object detection or surrogate interaction tasks. The authors propose a streamlined framework using a transformer-based architecture—termed HOI Transformer—that directly predicts HOI instances from the global image context. This method showcases competitive results compared to existing approaches, particularly emphasizing simplicity and efficiency.

Methodology

The innovation in this paper lies in its use of a transformer encoder-decoder framework for HOI detection. This method contrasts sharply with previous strategies that rely on decoupled pipelines of object detection followed by interaction classification. Such prior methods often suffered from sub-optimal performance due to their reliance on independent optimization of two stages, leading to inefficiencies in handling human-object dependencies and increased computational complexity due to exhaustive pairwise processing.

HOI Transformer improves upon these drawbacks by leveraging the self-attention mechanism of transformers to capture long-range dependencies and contextual information from the image in an exhaustive manner. The paper introduces a quintuple matching loss function that unifies predictions of human, object, their bounding boxes, and interactions in a direct prediction framework. This end-to-end approach not only simplifies the pipeline by removing the need for hand-designed components and complex post-processing but also achieves an improved accuracy over prior methods.

Experimental Results

On the HICO-DET and V-COCO benchmarks, HOI Transformer achieves notable performance improvements. Particularly on HICO-DET, the model achieves $26.61\%$ $AP$ and $52.9\%$ $AP_{role}$ on V-COCO, surpassing numerous state-of-the-art competitors. It achieves this without leveraging additional datasets or modalities such as human poses or language priors, often utilized by other methods to boost performance in two-stage processes.

Implications and Future Work

The introduction of a transformer-based architecture for HOI presents multiple advantages and implications:

1. Scalability: The ability to handle complex dependencies efficiently without increasing computational resources exponentially is a significant advantage. HOI Transformer’s successful application suggests promising scalability to other vision tasks requiring context-aware reasoning capabilities.
2. Conceptual Simplicity: By distilling the process into fewer steps and removing the need for extensive post-processing, this approach is easier to deploy and adapt to various scenarios, encouraging broader adaptation and integration into real-world applications.
3. Generalization: The transformer’s architecture supports natural generalization across varied scenarios due to its invariance properties and extensive contextual capture.

Future directions may explore enhancing the transformer’s efficiency in HOI by exploiting more focused attention mechanisms or integrating with other modalities for richer feature representations while keeping computational costs low. Additionally, further investigation into the model's interpretability—understanding how specific attention patterns correlate with human visual perception in interaction contexts—could yield insights for more intelligible AI systems.

In summary, the HOI Transformer approach represents a meaningful step towards more integrated and simpler methods for understanding complex visual scenes with human-object interactions. It paves a path for continued exploration into transformer-based architectures for broader AI-driven scene understanding tasks.