Non-local Aggregation for RGB-D Semantic Segmentation

Exploiting both RGB (2D appearance) and Depth (3D geometry) information can improve the performance of semantic segmentation. However, due to the inherent difference between the RGB and Depth information, it remains a challenging problem in how to integrate RGB-D features effectively. In this letter, to address this issue, we propose a Non-local Aggregation Network (NANet), with a well-designed Multi-modality Non-local Aggregation Module (MNAM), to better exploit the non-local context of RGB-D features at multi-stage. Compared with most existing RGB-D semantic segmentation schemes, which only exploit local RGB-D features, the MNAM enables the aggregation of non-local RGB-D information along both spatial and channel dimensions. The proposed NANet achieves comparable performances with state-of-the-art methods on popular RGB-D benchmarks, NYUDv2 and SUN-RGBD

Efficient Multi-Task Scene Analysis with RGB-D Transformers

Scene analysis is essential for enabling autonomous systems, such as mobile robots, to operate in real-world environments. However, obtaining a comprehensive understanding of the scene requires solving multiple tasks, such as panoptic segmentation, instance orientation estimation, and scene classification. Solving these tasks given limited computing and battery capabilities on mobile platforms is challenging. To address this challenge, we introduce an efficient multi-task scene analysis approach, called EMSAFormer, that uses an RGB-D Transformer-based encoder to simultaneously perform the aforementioned tasks. Our approach builds upon the previously published EMSANet. However, we show that the dual CNN-based encoder of EMSANet can be replaced with a single Transformer-based encoder. To achieve this, we investigate how information from both RGB and depth data can be effectively incorporated in a single encoder. To accelerate inference on robotic hardware, we provide a custom NVIDIA TensorRT extension enabling highly optimization for our EMSAFormer approach. Through extensive experiments on the commonly used indoor datasets NYUv2, SUNRGB-D, and ScanNet, we show that our approach achieves state-of-the-art performance while still enabling inference with up to 39.1 FPS on an NVIDIA Jetson AGX Orin 32 GB.Comment: To be published in IEEE International Joint Conference on Neural Networks (IJCNN) 202

Vereinheitlichte Anfrageverarbeitung in heterogenen und verteilten Multimediadatenbanken

Multimedia retrieval is an essential part of today's world. This situation is observable in industrial domains, e.g., medical imaging, as well as in the private sector, visible by activities in manifold Social Media platforms. This trend led to the creation of a huge environment of multimedia information retrieval services offering multimedia resources for almost any user requests. Indeed, the encompassed data is in general retrievable by (proprietary) APIs and query languages, but unfortunately a unified access is not given due to arising interoperability issues between those services. In this regard, this thesis focuses on two application scenarios, namely a medical retrieval system supporting a radiologist's workflow, as well as an interoperable image retrieval service interconnecting diverse data silos. The scientific contribution of this dissertation is split in three different parts: the first part of this thesis improves the metadata interoperability issue. Here, major contributions to a community-driven, international standardization have been proposed leading to the specification of an API and ontology to enable a unified annotation and retrieval of media resources. The second part issues a metasearch engine especially designed for unified retrieval in distributed and heterogeneous multimedia retrieval environments. This metasearch engine is capable of being operated in a federated as well as autonomous manner inside the aforementioned application scenarios. The remaining third part ensures an efficient retrieval due to the integration of optimization techniques for multimedia retrieval in the overall query execution process of the metasearch engine.Egal ob im industriellen Bereich oder auch im Social Media - multimediale Daten nehmen eine immer zentralere Rolle ein. Aus diesem fortlaufendem Entwicklungsprozess entwickelten sich umfangreiche Informationssysteme, die Daten für zahlreiche Bedürfnisse anbieten. Allerdings ist ein einheitlicher Zugriff auf jene verteilte und heterogene Landschaft von Informationssystemen in der Praxis nicht gewährleistet. Und dies, obwohl die Datenbestände meist über Schnittstellen abrufbar sind. Im Detail widmet sich diese Arbeit mit der Bearbeitung zweier Anwendungsszenarien. Erstens, einem medizinischen System zur Diagnoseunterstützung und zweitens einer interoperablen, verteilten Bildersuche. Der wissenschaftliche Teil der vorliegenden Dissertation gliedert sich in drei Teile: Teil eins befasst sich mit dem Problem der Interoperabilität zwischen verschiedenen Metadatenformaten. In diesem Bereich wurden maßgebliche Beiträge für ein internationales Standardisierungsverfahren entwickelt. Ziel war es, einer Ontologie, sowie einer Programmierschnittstelle einen vereinheitlichten Zugriff auf multimediale Informationen zu ermöglichen. In Teil zwei wird eine externe Metasuchmaschine vorgestellt, die eine einheitliche Anfrageverarbeitung in heterogenen und verteilten Multimediadatenbanken ermöglicht. In den Anwendungsszenarien wird zum einen auf eine föderative, als auch autonome Anfrageverarbeitung eingegangen. Abschließend werden in Teil drei Techniken zur Optimierung von verteilten multimedialen Anfragen präsentiert

24th International Conference on Information Modelling and Knowledge Bases

In the last three decades information modelling and knowledge bases have become essentially important subjects not only in academic communities related to information systems and computer science but also in the business area where information technology is applied. The series of European – Japanese Conference on Information Modelling and Knowledge Bases (EJC) originally started as a co-operation initiative between Japan and Finland in 1982. The practical operations were then organised by professor Ohsuga in Japan and professors Hannu Kangassalo and Hannu Jaakkola in Finland (Nordic countries). Geographical scope has expanded to cover Europe and also other countries. Workshop characteristic - discussion, enough time for presentations and limited number of participants (50) / papers (30) - is typical for the conference. Suggested topics include, but are not limited to: 1. Conceptual modelling: Modelling and specification languages; Domain-specific conceptual modelling; Concepts, concept theories and ontologies; Conceptual modelling of large and heterogeneous systems; Conceptual modelling of spatial, temporal and biological data; Methods for developing, validating and communicating conceptual models. 2. Knowledge and information modelling and discovery: Knowledge discovery, knowledge representation and knowledge management; Advanced data mining and analysis methods; Conceptions of knowledge and information; Modelling information requirements; Intelligent information systems; Information recognition and information modelling. 3. Linguistic modelling: Models of HCI; Information delivery to users; Intelligent informal querying; Linguistic foundation of information and knowledge; Fuzzy linguistic models; Philosophical and linguistic foundations of conceptual models. 4. Cross-cultural communication and social computing: Cross-cultural support systems; Integration, evolution and migration of systems; Collaborative societies; Multicultural web-based software systems; Intercultural collaboration and support systems; Social computing, behavioral modeling and prediction. 5. Environmental modelling and engineering: Environmental information systems (architecture); Spatial, temporal and observational information systems; Large-scale environmental systems; Collaborative knowledge base systems; Agent concepts and conceptualisation; Hazard prediction, prevention and steering systems. 6. Multimedia data modelling and systems: Modelling multimedia information and knowledge; Contentbased multimedia data management; Content-based multimedia retrieval; Privacy and context enhancing technologies; Semantics and pragmatics of multimedia data; Metadata for multimedia information systems. Overall we received 56 submissions. After careful evaluation, 16 papers have been selected as long paper, 17 papers as short papers, 5 papers as position papers, and 3 papers for presentation of perspective challenges. We thank all colleagues for their support of this issue of the EJC conference, especially the program committee, the organising committee, and the programme coordination team. The long and the short papers presented in the conference are revised after the conference and published in the Series of “Frontiers in Artificial Intelligence” by IOS Press (Amsterdam). The books “Information Modelling and Knowledge Bases” are edited by the Editing Committee of the conference. We believe that the conference will be productive and fruitful in the advance of research and application of information modelling and knowledge bases. Bernhard Thalheim Hannu Jaakkola Yasushi Kiyok

Artificial general intelligence: Proceedings of the Second Conference on Artificial General Intelligence, AGI 2009, Arlington, Virginia, USA, March 6-9, 2009

Artificial General Intelligence (AGI) research focuses on the original and ultimate goal of AI – to create broad human-like and transhuman intelligence, by exploring all available paths, including theoretical and experimental computer science, cognitive science, neuroscience, and innovative interdisciplinary methodologies. Due to the difficulty of this task, for the last few decades the majority of AI researchers have focused on what has been called narrow AI – the production of AI systems displaying intelligence regarding specific, highly constrained tasks. In recent years, however, more and more researchers have recognized the necessity – and feasibility – of returning to the original goals of the field. Increasingly, there is a call for a transition back to confronting the more difficult issues of human level intelligence and more broadly artificial general intelligence