Pose-aware Multi-level Feature Network for Human Object Interaction Detection (1909.08453v1)

Abstract: Reasoning human object interactions is a core problem in human-centric scene understanding and detecting such relations poses a unique challenge to vision systems due to large variations in human-object configurations, multiple co-occurring relation instances and subtle visual difference between relation categories. To address those challenges, we propose a multi-level relation detection strategy that utilizes human pose cues to capture global spatial configurations of relations and as an attention mechanism to dynamically zoom into relevant regions at human part level. Specifically, we develop a multi-branch deep network to learn a pose-augmented relation representation at three semantic levels, incorporating interaction context, object features and detailed semantic part cues. As a result, our approach is capable of generating robust predictions on fine-grained human object interactions with interpretable outputs. Extensive experimental evaluations on public benchmarks show that our model outperforms prior methods by a considerable margin, demonstrating its efficacy in handling complex scenes.

• The paper introduces PMFNet, a pose-aware multi-level feature network that integrates object and pose cues for refined HOI detection.
• It employs a novel zoom-in module with semantic attention to dynamically focus on critical human parts for improved interaction understanding.
• Experiments on V-COCO and HICO-DET demonstrate state-of-the-art performance, especially in detecting ambiguous interactions with small objects.

Pose-aware Multi-level Feature Network for Human Object Interaction Detection

The paper presented outlines a novel approach to detecting Human Object Interactions (HOI) by leveraging pose information to enhance feature representation in a multi-level architecture. Detecting human-object interactions plays a crucial role in understanding complex visual scenes, with applications in activity analysis, video understanding, and visual question answering. Traditional approaches focused mostly on object-level representation, which often results in imprecise predictions due to the lack of context and fine-grained detail required to differentiate subtle interaction changes.

Key Contributions and Methodology

1. Multi-level Feature Network: The authors introduce a multi-level feature network named PMFNet that integrates interaction context, object features, and detailed semantic part cues. This network is built upon a backbone ResNet-50-FPN that generates initial feature map representations. It achieves superior interpretability by providing a structural explanation of scene components.
2. Pose-aware Representation: Human pose estimations are utilized not only to comprehend spatial configurations globally but also to focus on informative human parts dynamically, thanks to a module termed 'zoom-in'. This approach leverages human poses to significantly augment the rich semantic content required for accurate and fine-grained interaction detection.
3. Modular Architecture:
   • Holistic Module: It considers the whole object and context by merging features from human, object, and their union region.
   • Zoom-in Module: It further focuses on minute human parts using part-crop features and refines them with spatial alignment and semantic attention mechanisms.
4. Adaptive Attention Mechanism: The zoom-in module incorporates a pose-aware semantic attention component that dynamically assigns importance to human parts that drive interactions during inference. This adaptive mechanism showcases an essential step towards explainability.
5. Performance and Evaluation:
   • The method demonstrated efficacy through experiments conducted on the V-COCO and HICO-DET datasets, achieving state-of-the-art performance in HOI detection. On V-COCO, PMFNet improved over existing results by a significant margin and provided an ablation paper to substantiate the contribution of each module.
   • Specifying detailed human interactions was particularly enhanced in images with small or ambiguously positioned objects, indicating the value of fine-grained, pose-aware reasoning.

Implications and Future Speculations

The novel integration of human pose cues with multi-level feature extraction positions PMFNet as a notable advance in HOI detection paradigms. Practically, this contribution allows for improved understanding of human interactions in complex scenes, which is vital for applications in autonomous systems, surveillance, and assistive technology in robotics.

Theoretically, the methodology highlights the impact of leveraging intermediate human-centric cues for relation reasoning over conventional object-centric methods. Future directions could expand into exploring unsupervised learning frameworks for HOI using this augmented representation, allowing expanded generalization across various visual tasks.

In conclusion, this paper provides insights into overcoming the limitations of traditional HOI detection by effectively utilizing pose and spatial features. As models continue to evolve towards more human-like understanding, methodologies employing such multi-level reasoning are likely to play pivotal roles in advancing computer vision systems.