13 accepted papers
Precise automated understanding of agricultural tasks such as disease identification is essential for the sustainable crop production. Recent advances in vision-language models (VLMs) are expected to further expand the range of agricultural tasks by facilitating human-model interaction through easy,…
Humans typically avert their gaze when faced with situations involving another person’s privacy, and humanoid robots should exhibit similar behaviors. Various approaches exist for privacy recognition, including an image privacy recognition model and a Large Vision-Language Model (LVLM). The former r
This paper presents a source-aware spatial self-supervised learning (SSL) method for sound event localization and detection (SELD) based on neural blind source separation (BSS). SELD involves estimating both the temporal activations of sound classes and their directions of arrival (DOAs) from multic…
"Neural Radiance Field (NeRF) methodologies have garnered considerable interest, particularly with the introduction of grid-based feature encoding (GFE) approaches such as Instant-NGP and TensoRF. Conventional NeRF employs positional encoding (PE) and represents a scene with a Multi-Layer Perceptron…
The ability to associate the current location with previously visited places is an essential aspect of autonomous ground robots. Unstructured environments such as planetary surfaces pose a significant challenge for robots because their terrain is less distinctive. Meanwhile, traversability must be a…
This paper proposes a low latency neural network architecture for event-based dense prediction tasks. Conventional architectures encode entire scene contents at a fixed rate regardless of their temporal characteristics. Instead, the proposed network encodes contents at a proper temporal scale depend…
Sensor fusion has become a popular topic in robotics. However, conventional fusion methods encounter many difficulties, such as data representation differences, sensor variations, and extrinsic calibration. For example, the calibration methods used for LiDAR-camera fusion often require manual operat…
Hyperparameter optimization (HPO) is a vital step in improving performance in deep learning (DL). Practitioners are often faced with the trade-off between multiple criteria, such as accuracy and latency. Given the high computational needs of DL and the growing demand for efficient HPO, the accelerat…
Perceptual Aliasing is one of the main problems in simultaneous localization and mapping (SLAM). Wrong associations between different places may lead to failure of the whole map. Research on structure information is rarely investigated among existing solutions to this problem. In cases of visual SLA…
This paper proposes a novel heterogeneous grid convolution that builds a graph-based image representation by exploiting heterogeneity in the image content, enabling adaptive, efficient, and controllable computations in a convolutional architecture. More concretely, the approach builds a data-adaptiv…
Hyperparameter optimization (HPO), formulated as black-box optimization (BBO), is recognized as essential for automation and high performance of machine learning approaches. The CMA-ES is a promising BBO approach with a high degree of parallelism, and has been applied to HPO tasks, often under paral…
Detecting sound source objects within visual observation is important for autonomous robots to comprehend surrounding environments. Since sounding objects have a large variety with different appearances in our living environments, labeling all sounding objects is impossible in practice. This calls f…
This study proposes a self-supervised method for detecting scene changes from an image pair. For mobile cameras such as drive recorders, to alleviate the camera viewpoints' difference, image alignment and change detection must be optimized simultaneously because they depend on each other. Moreover,…