12 accepted papers
"Throughout the history of computer vision, while research has explored the integration of images (visual) and point clouds (geometric), many advancements in image and 3D object recognition have tended to process these modalities separately. We aim to bridge this divide by integrating images and poi…
Visual inspection robots used in factories and outdoor environments require the ability to accurately recognize visual differences between similar objects and further verbalize the recognition results to present the differences to humans. Despite the application of Large Language Models (LLMs) and m…
We introduce a new task of question generation to eliminate the uncertainty of referring expressions in 3D indoor environments (3D-REQ). Referring to an object using natural language is one of the most common occurrences in daily human conversations; therefore, instructing robots to identify a certa…
Is it possible to complete Vision Transformer (ViT) pre-training without natural images and human-annotated labels? This question has become increasingly relevant in recent months because while current ViT pre-training tends to rely heavily on a large number of natural images and human-annotated lab…
Existing change captioning studies have mainly focused on a single change. However, detecting and describing multiple changed parts in image pairs is essential for enhancing adaptability to complex scenarios. We solve the above issues from three aspects: (i) We propose a simulation-based multi-chang…
In this letter, 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 app
The paper presents a pedestrian near-miss detector with temporal analysis that provides both pedestrian detection and risk-level predictions which are demonstrated on a self-collected database. Our work makes three primary contributions: (i) The framework of pedestrian near-miss detection is propose…
The paper presents an unsupervised out-of-context action (O2CA) paradigm that is based on facilitating understanding by separately presenting both human action and context within a video sequence. As a means of generating an unsupervised label, we comprehensively evaluate responses from action-based…
In this paper, we propose a novel approach for traffic accident anticipation through (i) Adaptive Loss for Early Anticipation (AdaLEA) and (ii) a large-scale self-annotated incident database. The proposed AdaLEA allows us to gradually learn an earlier anticipation as training progresses. The loss fu…
The purpose of this study is to determine whether current video datasets have sufficient data for training very deep convolutional neural networks (CNNs) with spatio-temporal three-dimensional (3D) kernels. Recently, the performance levels of 3D CNNs in the field of action recognition have improved…
Because of their recent introduction, self-driving cars and advanced driver assistance system (ADAS) equipped vehicles have had little opportunity to learn, the dangerous traffic (including near-miss incident) scenarios that provide normal drivers with strong motivation to drive safely. Accordingly,…
Many algorithms of computer vision use light sources to illuminate objects to actively create situation appropriate to extract their characteristics. For example, the shape and reflectance are measured by a projector-camera system, and some human-machine or VR systems use projectors and displays for…