DRIV100: In-The-Wild Multi-Domain Dataset and Evaluation for Real-World Domain Adaptation of Semantic Segmentation

Together with the recent advances in semantic segmentation, many domain adaptation methods have been proposed to overcome the domain gap between training and deployment environments. However, most previous studies use limited combinations of source/target datasets, and domain adaptation techniques have never been thoroughly evaluated in a more challenging and diverse set of target domains. This work presents a new multi-domain dataset DRIV100 for benchmarking domain adaptation techniques on in-the-wild road-scene videos collected from the Internet. The dataset consists of pixel-level annotations for 100 videos selected to cover diverse scenes/domains based on two criteria; human subjective judgment and an anomaly score judged using an existing road-scene dataset. We provide multiple manually labeled ground-truth frames for each video, enabling a thorough evaluation of video-level domain adaptation where each video independently serves as the target domain. Using the dataset, we quantify domain adaptation performances of state-of-the-art methods and clarify the potential and novel challenges of domain adaptation techniques. The dataset is available at https://doi.org/10.5281/zenodo.4389243.

Development of a Vertex Finding Algorithm using Recurrent Neural Network

Deep learning is a rapidly-evolving technology with possibility to significantly improve physics reach of collider experiments. In this study we developed a novel algorithm of vertex finding for future lepton colliders such as the International Linear Collider. We deploy two networks; one is simple fully-connected layers to look for vertex seeds from track pairs, and the other is a customized Recurrent Neural Network with an attention mechanism and an encoder-decoder structure to associate tracks to the vertex seeds. The performance of the vertex finder is compared with the standard ILC reconstruction algorithm.

Procams-Based Cybernetics

Procams-based cybernetics is a unique, emerging research field, which aims at enhancing and supporting our activities by naturally connecting human and computers/machines as a cooperative integrated system via projector-camera systems (procams). It rests on various research domains such as virtual/augmented reality, computer vision, computer graphics, projection display, human computer interface, human robot interaction and so on. This laboratory presentation provides a brief history including recent achievements of our procams-based cybernetics project.