Mario Fritz


Ontology type: schema:Person     


Person Info

NAME

Mario

SURNAME

Fritz

Publications in SciGraph latest 50 shown

  • 2021-09-14 Semantic Bottlenecks: Quantifying and Improving Inspectability of Deep Representations in INTERNATIONAL JOURNAL OF COMPUTER VISION
  • 2021-03-17 Semantic Bottlenecks: Quantifying and Improving Inspectability of Deep Representations in PATTERN RECOGNITION
  • 2021-03-17 Haar Wavelet Based Block Autoregressive Flows for Trajectories in PATTERN RECOGNITION
  • 2021-03-17 Long-Tailed Recognition Using Class-Balanced Experts in PATTERN RECOGNITION
  • 2021 IReEn: Reverse-Engineering of Black-Box Functions via Iterative Neural Program Synthesis in MACHINE LEARNING AND PRINCIPLES AND PRACTICE OF KNOWLEDGE DISCOVERY IN DATABASES
  • 2021 SampleFix: Learning to Generate Functionally Diverse Fixes in MACHINE LEARNING AND PRINCIPLES AND PRACTICE OF KNOWLEDGE DISCOVERY IN DATABASES
  • 2020-11-17 Inclusive GAN: Improving Data and Minority Coverage in Generative Models in COMPUTER VISION – ECCV 2020
  • 2020-11-03 Segmentations-Leak: Membership Inference Attacks and Defenses in Semantic Image Segmentation in COMPUTER VISION – ECCV 2020
  • 2020-11-03 Towards Automated Testing and Robustification by Semantic Adversarial Data Generation in COMPUTER VISION – ECCV 2020
  • 2020-03-17 Editor’s Note in INTERNATIONAL JOURNAL OF COMPUTER VISION
  • 2020 Body Shape Privacy in Images: Understanding Privacy and Preventing Automatic Shape Extraction in COMPUTER VISION – ECCV 2020 WORKSHOPS
  • 2020 Synthetic Convolutional Features for Improved Semantic Segmentation in COMPUTER VISION – ECCV 2020 WORKSHOPS
  • 2019-09-10 Towards Reverse-Engineering Black-Box Neural Networks in EXPLAINABLE AI: INTERPRETING, EXPLAINING AND VISUALIZING DEEP LEARNING
  • 2019-01-23 Workshop on Interactive and Adaptive Learning in an Open World in COMPUTER VISION – ECCV 2018 WORKSHOPS
  • 2019-01-23 Answering Visual What-If Questions: From Actions to Predicted Scene Descriptions in COMPUTER VISION – ECCV 2018 WORKSHOPS
  • 2018-10-06 Diverse Conditional Image Generation by Stochastic Regression with Latent Drop-Out Codes in COMPUTER VISION – ECCV 2018
  • 2018-10-06 A Hybrid Model for Identity Obfuscation by Face Replacement in COMPUTER VISION – ECCV 2018
  • 2018-02-01 Advanced Steel Microstructural Classification by Deep Learning Methods in SCIENTIFIC REPORTS
  • 2017-08-29 Ask Your Neurons: A Deep Learning Approach to Visual Question Answering in INTERNATIONAL JOURNAL OF COMPUTER VISION
  • 2017-08-15 Learning Dilation Factors for Semantic Segmentation of Street Scenes in PATTERN RECOGNITION
  • 2017-03-11 Towards Segmenting Consumer Stereo Videos: Benchmark, Baselines and Ensembles in COMPUTER VISION – ACCV 2016
  • 2016-11-24 VConv-DAE: Deep Volumetric Shape Learning Without Object Labels in COMPUTER VISION – ECCV 2016 WORKSHOPS
  • 2016-09-17 Faceless Person Recognition: Privacy Implications in Social Media in COMPUTER VISION – ECCV 2016
  • 2016-08-27 Learning to Select Long-Track Features for Structure-From-Motion and Visual SLAM in PATTERN RECOGNITION
  • 2014 Ubic: Bridging the Gap between Digital Cryptography and the Physical World in COMPUTER SECURITY - ESORICS 2014
  • 2013 Semi-Supervised Learning on a Budget: Scaling Up to Large Datasets in COMPUTER VISION – ACCV 2012
  • 2013 A Category-Level 3D Object Dataset: Putting the Kinect to Work in CONSUMER DEPTH CAMERAS FOR COMPUTER VISION
  • 2013 The Pooled NBNN Kernel: Beyond Image-to-Class and Image-to-Image in COMPUTER VISION – ACCV 2012
  • 2012 Active Metric Learning for Object Recognition in PATTERN RECOGNITION
  • 2012 Recognizing Materials from Virtual Examples in COMPUTER VISION – ECCV 2012
  • 2012 Sparselet Models for Efficient Multiclass Object Detection in COMPUTER VISION – ECCV 2012
  • 2011 Pick Your Neighborhood – Improving Labels and Neighborhood Structure for Label Propagation in PATTERN RECOGNITION
  • 2010 Multi-modal Learning in COGNITIVE SYSTEMS
  • 2010 Adapting Visual Category Models to New Domains in COMPUTER VISION – ECCV 2010
  • 2010 Categorical Perception in COGNITIVE SYSTEMS
  • 2008 Hierarchical Support Vector Random Fields: Joint Training to Combine Local and Global Features in COMPUTER VISION – ECCV 2008
  • 2006 Towards Unsupervised Discovery of Visual Categories in PATTERN RECOGNITION
  • 2004 On the Significance of Real-World Conditions for Material Classification in COMPUTER VISION - ECCV 2004
  • JSON-LD is the canonical representation for SciGraph data.

    TIP: You can open this SciGraph record using an external JSON-LD service: JSON-LD Playground Google SDTT

    [
      {
        "@context": "https://springernature.github.io/scigraph/jsonld/sgcontext.json", 
        "affiliation": [
          {
            "affiliation": {
              "id": "http://www.grid.ac/institutes/grid.507511.7", 
              "type": "Organization"
            }, 
            "isCurrent": true, 
            "type": "OrganizationRole"
          }, 
          {
            "id": "http://www.grid.ac/institutes/grid.6546.1", 
            "type": "Organization"
          }, 
          {
            "id": "http://www.grid.ac/institutes/grid.47840.3f", 
            "type": "Organization"
          }, 
          {
            "id": "http://www.grid.ac/institutes/grid.185107.a", 
            "type": "Organization"
          }, 
          {
            "id": "http://www.grid.ac/institutes/grid.4372.2", 
            "type": "Organization"
          }, 
          {
            "id": "http://www.grid.ac/institutes/grid.11749.3a", 
            "type": "Organization"
          }, 
          {
            "id": "http://www.grid.ac/institutes/grid.419528.3", 
            "type": "Organization"
          }, 
          {
            "id": "http://www.grid.ac/institutes/grid.5037.1", 
            "type": "Organization"
          }
        ], 
        "familyName": "Fritz", 
        "givenName": "Mario", 
        "id": "sg:person.013361072755.17", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.013361072755.17"
        ], 
        "sdDataset": "persons", 
        "sdDatePublished": "2022-10-01T07:17", 
        "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
        "sdPublisher": {
          "name": "Springer Nature - SN SciGraph project", 
          "type": "Organization"
        }, 
        "sdSource": "s3://com-springernature-scigraph/baseset/20221001/entities/gbq_results/person/person_801.jsonl", 
        "type": "Person"
      }
    ]
     

    Download the RDF metadata as:  json-ld nt turtle xml License info

    HOW TO GET THIS DATA PROGRAMMATICALLY:

    JSON-LD is a popular format for linked data which is fully compatible with JSON.

    curl -H 'Accept: application/ld+json' 'https://scigraph.springernature.com/person.013361072755.17'

    N-Triples is a line-based linked data format ideal for batch operations.

    curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/person.013361072755.17'

    Turtle is a human-readable linked data format.

    curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/person.013361072755.17'

    RDF/XML is a standard XML format for linked data.

    curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/person.013361072755.17'


     

    This table displays all metadata directly associated to this object as RDF triples.

    30 TRIPLES      10 PREDICATES      18 URIs      7 LITERALS      2 BLANK NODES

    Subject Predicate Object
    1 sg:person.013361072755.17 schema:affiliation Na3f16d9efef44e53b1326d125e298d6b
    2 grid-institutes:grid.11749.3a
    3 grid-institutes:grid.185107.a
    4 grid-institutes:grid.419528.3
    5 grid-institutes:grid.4372.2
    6 grid-institutes:grid.47840.3f
    7 grid-institutes:grid.5037.1
    8 grid-institutes:grid.6546.1
    9 schema:familyName Fritz
    10 schema:givenName Mario
    11 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.013361072755.17
    12 schema:sdDatePublished 2022-10-01T07:17
    13 schema:sdLicense https://scigraph.springernature.com/explorer/license/
    14 schema:sdPublisher N340c42118b9a45b591e46b96799d189f
    15 sgo:license sg:explorer/license/
    16 sgo:sdDataset persons
    17 rdf:type schema:Person
    18 N340c42118b9a45b591e46b96799d189f schema:name Springer Nature - SN SciGraph project
    19 rdf:type schema:Organization
    20 Na3f16d9efef44e53b1326d125e298d6b schema:affiliation grid-institutes:grid.507511.7
    21 sgo:isCurrent true
    22 rdf:type schema:OrganizationRole
    23 grid-institutes:grid.11749.3a schema:Organization
    24 grid-institutes:grid.185107.a schema:Organization
    25 grid-institutes:grid.419528.3 schema:Organization
    26 grid-institutes:grid.4372.2 schema:Organization
    27 grid-institutes:grid.47840.3f schema:Organization
    28 grid-institutes:grid.5037.1 schema:Organization
    29 grid-institutes:grid.507511.7 schema:Organization
    30 grid-institutes:grid.6546.1 schema:Organization
     




    Preview window. Press ESC to close (or click here)


    ...