Daimler AG

About: Daimler AG is a company organization based out in Stuttgart, Germany. It is known for research contribution in the topics: Internal combustion engine & Exhaust gas. The organization has 36582 authors who have published 43881 publications receiving 375581 citations.

The Cityscapes Dataset for Semantic Urban Scene Understanding

Abstract: Visual understanding of complex urban street scenes is an enabling factor for a wide range of applications. Object detection has benefited enormously from large-scale datasets, especially in the context of deep learning. For semantic urban scene understanding, however, no current dataset adequately captures the complexity of real-world urban scenes. To address this, we introduce Cityscapes, a benchmark suite and large-scale dataset to train and test approaches for pixel-level and instance-level semantic labeling. Cityscapes is comprised of a large, diverse set of stereo video sequences recorded in streets from 50 different cities. 5000 of these images have high quality pixel-level annotations, 20 000 additional images have coarse annotations to enable methods that leverage large volumes of weakly-labeled data. Crucially, our effort exceeds previous attempts in terms of dataset size, annotation richness, scene variability, and complexity. Our accompanying empirical study provides an in-depth analysis of the dataset characteristics, as well as a performance evaluation of several state-of-the-art approaches based on our benchmark.

The Cityscapes Dataset for Semantic Urban Scene Understanding

Abstract: Visual understanding of complex urban street scenes is an enabling factor for a wide range of applications. Object detection has benefited enormously from large-scale datasets, especially in the context of deep learning. For semantic urban scene understanding, however, no current dataset adequately captures the complexity of real-world urban scenes. To address this, we introduce Cityscapes, a benchmark suite and large-scale dataset to train and test approaches for pixel-level and instance-level semantic labeling. Cityscapes is comprised of a large, diverse set of stereo video sequences recorded in streets from 50 different cities. 5000 of these images have high quality pixel-level annotations; 20000 additional images have coarse annotations to enable methods that leverage large volumes of weakly-labeled data. Crucially, our effort exceeds previous attempts in terms of dataset size, annotation richness, scene variability, and complexity. Our accompanying empirical study provides an in-depth analysis of the dataset characteristics, as well as a performance evaluation of several state-of-the-art approaches based on our benchmark.

Generative Programming: Methods, Tools, and Applications

Abstract: 1. What Is This Book About? From Handcrafting to Automated Assembly Lines. Generative Programming. Benefits and Applicability. I. ANALYSIS AND DESIGN METHODS AND TECHNIQUES. 2. Domain Engineering. Why Is This Chapter Worth Reading? What Is Domain Engineering? Domain Analysis. Domain Design and Domain Implementation. Application Engineering. Product-Line Practices. Key Domain Engineering Concepts. Domain. Domain Scope and Scoping. Relationships between Domains. Features and Feature Models. Method Tailoring and Specialization. Survey of Domain Analysis and Domain Engineering Methods. Feature-Oriented Domain Analysis (FODA). Organization Domain Modeling (ODM). Draco. Capture. Domain Analysis and Reuse Environment (DARE). Domain-Specific Software Architecture (DSSA) Approach. Algebraic Approach. Other Approaches. Domain Engineering and Related Approaches. Historical Notes. Summary. 3. Domain Engineering and Object-Oriented Analysis and Design. Why Is This Chapter Worth Reading? OO Technology and Reuse. Solution Space. Problem Space. Relationship between Domain Engineering and Object-Oriented Analysis and Design (OOA/D) Methods. Aspects of Integrating Domain Engineering and OOA/D Methods. Horizontal versus Vertical Methods. Selected Methods. Rational Unified Process. 00ram. Reuse-Driven Software Engineering Business (RSEB). FeatuRSEB. Domain Engineering Method for Reusable Algorithmic Libraries (DEMRAL). 4. Feature Modeling. Why Is This Chapter Worth Reading? Features Revisited. Feature Modeling. Feature Models. Feature Diagrams. Other Infon-Nation Associated with Feature Diagrams in a Feature Model. Assigning Priorities to Variable Features. Availability Sites, Binding Sites, and Binding Modes. Relationship between Feature Diagrams and Other Modeling Notations and Implementation Techniques. Single Inheritance. Multiple Inheritance. Parameterized Inheritance. Static Parameterization. Dynamic Parameterization. Implementing Constraints. Tool Support for Feature Models. Frequently Asked Questions about Feature Diagrams. Feature Modeling Process. How to Find Features. Role of Variability in Modeling. 5. The Process of Generative Programming. Why Is This Chapter Worth Reading? Generative Domain Models. Main Development Steps in Generative Programming. Adapting Domain Engineering for Generative Programming. Domain-Specific Languages. DEMRAL: Example of a Domain Engineering Method for Generative Programming. Outline of DEMRAL. Domain Analysis. Domain Definition. Domain Modeling. Domain Design. Scope Domain Model for Implementation. Identify Packages. Develop Target Architectures and Identify the Implementation Components. Identify User DSLs. Identify Interactions between DSLs. Specify DSLs and Their Translation. Configuration DSLs. Expression DSLs. Domain Implementation. II. IMPLEMENTATION TECHNOLOGIES. 6. Generic Programming. Why Is This Chapter Worth Reading? What Is Generic Programming? Generic versus Generative Programming. Generic Parameters. Parametric versus Subtype Polymorphism. Genericity in Java. Bounded versus Unbounded Polymorphism. A Fresh Look at Polymorphism. Parameterized Components. Parameterized Programming. Types, Interfaces, and Specifications. Adapters. Vertical and Horizontal Parameters. Module Expressions. C++ Standard Template Library. Iterators. Freestanding Functions versus Member Functions. Generic Methodology. Historical Notes. 7. Component-Oriented Template-Based C++ Programming Techniques. Why Is This Chapter Worth Reading? Types of System Configuration. C++ Support for Dynamic Configuration. C++ Support for Static Configuration. Static Typing. Static Binding. Inlining. Templates. Parameterized Inheritance. typedefs. Member Types. Nested Classes. Prohibiting Certain Template Instantiations. Static versus Dynamic Parameterization. Wrappers Based on Parameterized Inheritance. Template Method Based on Parameterized Inheritance. Parameterizing Binding Mode. Consistent Parameterization of Multiple Components. Static Interactions between Components. Components with Influence. Components under Influence. Structured Configurations. Recursive Components. Intelligent Configuration. 8. Aspect-Oriented Decomposition and Composition. Why Is This Chapter Worth Reading? What Is Aspect-Oriented Programming? Aspect-Oriented Decomposition Approaches. Subject-Oriented Programming. Composition Filters. Demeter / Adaptive Programming. Aspect-Oriented Decomposition and Domain Engineering. How Aspects Arise. Composition Mechanisms. Requirements on Composition Mechanisms. Example: Synchronizing a Bounded Buffer. "Tangled" Synchronized Stack. Separating Synchronization Using Design Patterns. Separating Synchronization Using SOP. Some Problems with Design Patterns and Some Solutions. Implementing Noninvasive, Dynamic Composition in Smalltalk. Kinds of Crosscutting. How to Express Aspects in Programming Languages. Separating Synchronization Using AspectJ Cool. Implementing Dynamic Cool in Smalltalk. Implementation Technologies for Aspect-Oriented Programming. Technologies for Implementing Aspect-Specific Abstractions. Technologies for Implementing Weaving. AOP and Specialized Language Extensions. AOP and Active Libraries. Final Remarks. 9. Generators. Why Is This Chapter Worth Reading? What Are Generators? Transformational Model of Software Development. Technologies for Building Generators. Compositional versus Transformational Generators. Kinds of Transformations. Compiler Transformations. Source-to-Source Transformations. Transformation Systems. Scheduling Transformations. Existing Transformation Systems and Their Applications. Selected Approaches to Generation. Draco. GenVoca. Approaches Based on Algebraic Specifications. 10. Static Metaprogramming in C++. Why Is This Chapter Worth Reading? What Is Metaprogramming? A Quick Tour of Metaprogramming. Static Metaprogramming. C++ as a Two-Level Language. Functional Flavor of the Static Level. Class Templates as Functions. Integers and Types as Data. Symbolic Names Instead of Variables. Constant Initialization and typedef-Statements Instead of Assignment. Template Recursion Instead of Loops. Conditional Operator and Template Specialization as Conditional Constructs. Template Metaprogramming. Template Metafunctions. Metafinctions as Arguments and Return Values of Other Metafinctions. Representing Metainformation. Member Traits. Traits Classes. Traits Templates. Example: Using Template Metafunctions and Traits Templates to Implement Type Promotions. Compile-Time Lists and Trees as Nested Templates. Compile-Time Control Structures. Explicit Selection Constructs. Template Recursion as a Looping Construct. Explicit Looping Constructs. Code Generation. Simple Code Selection. Composing Templates. Generators Based on Expression Templates. Recursive Code Expansion. Explicit Loops for Generating Code. Example: Using Static Execute Loops to Test Metafunctions. Partial Evaluation in C++. Workarounds for Partial Template Specialization. Problems of Template Metaprogramming. Historical Notes. 11. Intentional Programming. Why Is This Chapter Worth Reading? What Is Intentional Programming? Technology behind IP. System Architecture. Representing Programs in IP: The Source Graph. Source Graph + Methods = Active Source. Working with the IP Programming Environment. Editing. Further Capabilities of the IP Editor. Extending the IP System with New Intentions. Advanced Topics. Questions, Methods, and a Frameworklike Organization. Source-Pattem-Based Polymorphism. Methods as Visitors. Asking Questions Synchronously and Asynchronously. Reduction. The Philosophy behind IP. Why Do We Need Extendible Programming Environments? or What Is the Problem with Fixed Programming Languages? Moving Focus from Fixed Languages to Language Features and the Emergence of an Intention Market. Intentional Programming and Component-Based Development. Frequently Asked Questions. Summary. III. APPLICATION EXAMPLES. 12. List Container. Why Is This Chapter Worth Reading? Overview. Domain Analysis. Domain Design. Implementation Components. Manual Assembly. Specifying Lists. The Generator. Extensions. 13. Bank Account. Why Is This Chapter Worth Reading? The Successful Programming Shop. Design Pattems, Frameworks, and Components. Domain Engineering and Generative Programming. Feature Modeling. Architecture Design. Implementation Components. Configurable Class Hierarchies. Designing a Domain-Specific Language. Bank Account Generator. Testing Generators and Their Products. 14. Generative Matrix Computation Library (GMCL). Why Is This Chapter Worth Reading? Why Matrix Computations? Domain Analysis. Domain Definition. Domain Modeling. Domain Design and Implementation. Matrix Type Generation. Generating Code for Matrix Expressions. Implementing the Matrix Component in IP. APPENDICES. Appendix A: Conceptual Modeling. What Are Concepts? Theories of Concepts. Basic Terminology. The Classical View. The Probabilistic View. The Exemplar View. Summary of the Three Views. Important Issues Concerning Concepts. Stability of Concepts. Concept Core. Informational Contents of Features. Feature Composition and Relationships between Features. Quality of Features. Abstraction and Generalization. Conceptual Modeling, Object-Orientation, and Software Reuse. Appendix B: Instance-Specific Extension Protocol for Smalltalk. Appendix C: Protocol for Attaching Listener Objects in Smalltalk. Appendix D: Glossary of Matrix Computation Terms. Appendix E: Metafunction for Evaluating Dependency Tables. Glossary of Generative Programming Terms. References. Index. 020130977T04062001