Interacted Object Grounding in Spatio-Temporal Human-Object Interactions (2412.19542v2)

Abstract: Spatio-temporal Human-Object Interaction (ST-HOI) understanding aims at detecting HOIs from videos, which is crucial for activity understanding. However, existing whole-body-object interaction video benchmarks overlook the truth that open-world objects are diverse, that is, they usually provide limited and predefined object classes. Therefore, we introduce a new open-world benchmark: Grounding Interacted Objects (GIO) including 1,098 interacted objects class and 290K interacted object boxes annotation. Accordingly, an object grounding task is proposed expecting vision systems to discover interacted objects. Even though today's detectors and grounding methods have succeeded greatly, they perform unsatisfactorily in localizing diverse and rare objects in GIO. This profoundly reveals the limitations of current vision systems and poses a great challenge. Thus, we explore leveraging spatio-temporal cues to address object grounding and propose a 4D question-answering framework (4D-QA) to discover interacted objects from diverse videos. Our method demonstrates significant superiority in extensive experiments compared to current baselines. Data and code will be publicly available at https://github.com/DirtyHarryLYL/HAKE-AVA.

• The paper introduces a novel 4D-QA framework and GIO benchmark that significantly improve object grounding in diverse spatio-temporal human-object interactions.
• It leverages multi-modal feature extraction and transformer decoders to effectively integrate spatio-temporal cues for precise localization.
• Results show marked improvements over conventional methods, advancing activity recognition and open-world object detection.

Interacted Object Grounding in Spatio-Temporal Human-Object Interactions: An Advanced Framework and Evaluation Benchmark

The paper "Interacted Object Grounding in Spatio-Temporal Human-Object Interactions" addresses an important challenge within the domain of computer vision and activity understanding: the detection and grounding of interacted objects within spatio-temporal human-object interactions (ST-HOI) from videos. This task is critical for advancing comprehensive activity recognition and has applications in diverse fields such as surveillance, robotics, and autonomous systems.

Overview of the Research

The authors identify a significant gap in existing ST-HOI benchmarks, which predominantly focus on predefined and limited object classes. The paper introduces a new open-world benchmark called Grounding Interacted Objects (GIO), which encompasses a diverse set of 1,098 object classes and 290,000 interacted object boxes.

A compelling aspect of this research is the identification of the limitations of current vision systems in dealing with rare and diverse objects present in open-world settings. The proposed GIO benchmark and correlated object grounding task effectively highlight these challenges, thereby providing a platform for further exploration and improvement in ST-HOI understanding.

Proposed Methodology

The research presents a novel 4D question-answering framework (4D-QA) designed to address the complexities of the object grounding task in the context of ST-HOI. By leveraging spatio-temporal cues, the authors improve the localizing capabilities of vision systems for interacted objects. This approach is particularly relevant given the inadequacies found in existent detectors and grounding methods when applied to the diverse object scenarios encapsulated by GIO.

The framework incorporates modern objectness detectors like SAM to handle candidate generation, followed by a multi-modal feature extraction process that utilizes context, object, language interaction, and 4D human-object features. The subsequent 2D and 3D transformer decoders integrate these features to effectively ground the interacted objects, demonstrating an appreciable performance over current baseline methods.

Results and Contributions

The numerical results of the proposed 4D-QA method on the GIO dataset illustrate significant improvements over conventional baselines, including image/video-based HOI models, open-vocabulary detection models, and visual grounding models. Specifically, the method consistently achieves higher weighted mean Intersection over Union (mIoU_w) and mean Average Precision at varying thresholds, underscoring its efficacy in handling complex and varied ST-HOI scenarios.

The contributions of this work are substantial:

1. The introduction of the GIO benchmark with expansive diversity in object classes provides a robust platform for evaluating open-world ST-HOI systems.
2. The framing of a novel grounding task aimed at interacted object discovery advances the paper of finer-grained activity understanding.
3. The development of the 4D-QA framework marks a noteworthy progression in multi-modal feature integration for object grounding tasks, offering new insights into improving activity understanding systems.

Implications and Future Directions

Practically, the GIO benchmark and the proposed methodology have the potential to enhance systems for security, healthcare, and various human-object interaction applications. Theoretically, the incorporation of 4D features into ST-HOI tasks opens new avenues for research, facilitating deeper explorations into multi-modal interaction cues and their impact on object detection and activity recognition. Moreover, the potential integration of such advanced models with LLMs for semantic understanding could foster further advancements in autonomous understanding of human activities.

In conclusion, this paper provides a comprehensive evaluation framework and an innovative methodological contribution that collectively address pressing challenges in the field of human-object interaction detection and understanding, paving the way for future advancements in activity recognition and contextual awareness in video analysis tasks.