In this paper, we propose a framework that recognizes and describes changes that occur in a scene observed from multiple viewpoints in natural language text. The ability to recognize and describe changes that occurred in a 3D scene plays an essential role in a variety of human-robot interaction applications. However, most current 3D vision studies have focused on understanding the static 3D scene. Existing scene change captioning approaches recognize and generate change captions from single-view images. Those methods have limited ability to deal with camera movement, object occlusion, which are common in real-world settings. To resolve these problems, we propose a framework that observes every scene from multiple viewpoints and describes the scene change based on an understanding of the underlying 3D structure of scenes. We build three synthetic datasets consisting of primitive 3D object and scanned real object models for evaluation. The results indicate that our method outperforms the previous state-of-the-art 2D-based method by a large margin in terms of sentence generation and change understanding correctness. In addition, our method is more robust to camera movements compared to the previous method and also performs better for scenes with occlusions. Moreover, our method also shows encouraging results in a realistic scene-setting, which indicates the possibility of adapting our framework to a more complicated and extensive scene-settings.

This study proposes a framework for describing a scene change using natural language text based on indoor scene observations conducted before and after a scene change. The recognition of scene changes plays an essential role in a variety of real-world applications, such as scene anomaly detection. Most scene understanding research has focused on static scenes. Most existing scene change captioning methods detect scene changes from single-view RGB images, neglecting the underlying three-dimensional structures. Previous three-dimensional scene change captioning methods use simulated scenes consisting of geometry primitives, making it unsuitable for real-world applications. To solve these problems, we automatically generated large-scale indoor scene change caption datasets. We propose an end-to-end framework for describing scene changes from various input modalities, namely, RGB images, depth images, and point cloud data, which are available in most robot applications. We conducted experiments with various input modalities and models and evaluated model performance using datasets with various levels of complexity. Experimental results show that the models that combine RGB images and point cloud data as input achieve high performance in sentence generation and caption correctness and are robust for change type understanding for datasets with high complexity. The developed datasets and models contribute to the study of indoor scene change understanding.

This paper proposes a framework that allows the observation of a scene iteratively to answer a given question about the scene. Conventional visual question answering (VQA) methods are designed to answer given questions based on single-view images. However, in real-world applications, such as human–robot interaction (HRI), in which camera angles and occluded scenes must be considered, answering questions based on single-view images might be difficult. Since HRI applications make it possible to observe a scene from multiple viewpoints, it is reasonable to discuss the VQA task in multi-view settings. In addition, because it is usually challenging to observe a scene from arbitrary viewpoints, we designed a framework that allows the observation of a scene actively until the necessary scene information to answer a given question is obtained. The proposed framework achieves comparable performance to a state-of-the-art method in question answering and simultaneously decreases the number of required observation viewpoints by a significant margin. Additionally, we found our framework plausibly learned to choose better viewpoints for answering questions, lowering the required number of camera movements. Moreover, we built a multi-view VQA dataset based on real images. The proposed framework shows high accuracy (94.01%) for the unseen real image dataset.

In the field of embodied AI, vision-and-language navigation (VLN) is a crucial and challenging multi-modal task. Specifically, outdoor VLN involves an agent navigating within a graph-based environment, while simultaneously interpreting information from real-world urban environments and natural language instructions. Existing outdoor VLN models predict actions using a combination of panorama and instruction features. However, these methods may cause the agent to struggle to understand complicated outdoor environments and ignore the details in the environments to fail to navigate. Human navigation often involves the use of specific objects as reference landmarks when navigating to unfamiliar places, providing a more rational and efficient approach to navigation. Inspired by this natural human behavior, we propose an object-level alignment module (OAlM), which guides the agent to focus more on object tokens mentioned in the instructions and recognize these landmarks during navigation. By treating these landmarks as sub-goals, our method effectively decomposes a long-range path into a series of shorter paths, ultimately improving the agent’s overall performance. In addition to enabling better object recognition and alignment, our proposed OAlM also fosters a more robust and adaptable agent capable of navigating complex environments. This adaptability is particularly crucial for real-world applications where environmental conditions can be unpredictable and varied. Experimental results show our OAlM is a more object-focused model, and our approach outperforms all metrics on a challenging outdoor VLN Touchdown dataset, exceeding the baseline by 3.19% on task completion (TC). These results highlight the potential of leveraging object-level information in the form of sub-goals to improve navigation performance in embodied AI systems, paving the way for more advanced and efficient outdoor navigation.

Traditional Vision-and-Language Navigation (VLN) tasks require an agent to navigate static environments using natural language instructions. However, real-world road conditions such as vehicle movements, traffic signal fluctuations, pedestrian activity, and weather variations are dynamic and continually changing. These factors significantly impact an agent’s decision-making ability, underscoring the limitations of current VLN models, which do not accurately reflect the complexities of real-world navigation. To bridge this gap, we propose a novel task called Dynamic Vision-and-Language Navigation (DynamicVLN), incorporating various dynamic scenarios to enhance the agent’s decision-making abilities and adaptability. By redefining the VLN task, we emphasize that a robust and generalizable agent should not rely solely on predefined instructions but must also demonstrate reasoning skills and adaptability to unforeseen events. Specifically, we have designed ten scenarios that simulate the challenges of dynamic navigation and developed a dedicated dataset of 11,261 instances using the CARLA simulator (ver.0.9.13) and large language model to provide realistic training conditions. Additionally, we introduce a baseline model that integrates advanced perception and decision-making modules, enabling effective navigation and interpretation of the complexities of dynamic road conditions. This model showcases the ability to follow natural language instructions while dynamically adapting to environmental cues. Our approach establishes a benchmark for developing agents capable of functioning in real-world, dynamic environments and extending beyond the limitations of static VLN tasks to more practical and versatile applications.

In recent years, advancements in the interaction and collaboration between humans and have garnered significant attention. Social intelligence plays a crucial role in facilitating natural interactions and seamless communication between humans and Artificial Intelligence (AI). To assess AI’s ability to understand human interactions and the components necessary for such comprehension, datasets like Social-IQ have been developed. However, these datasets often rely on a simplistic question-and-answer format and lack justifications for the provided answers. Furthermore, existing methods typically produce direct answers by selecting from predefined choices without generating intermediate outputs, which hampers interpretability and reliability. To address these limitations, we conducted a comprehensive evaluation of AI methods on a video-based Question Answering (QA) benchmark focused on human interactions, leveraging additional annotations related to human responses. Our analysis highlights significant differences between human and AI response patterns and underscores critical shortcomings in current benchmarks. We anticipate that these findings will guide the creation of more advanced datasets and represent an important step toward achieving natural communication between humans and AI.