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Auflistung nach Autor:in "Frintrop, Simone"

1 - 4 von 4
  • Zeitschriftenartikel
    Attention-Based Detection of Unknown Objects in a Situated Vision Framework
    (KI - Künstliche Intelligenz: Vol. 27, No. 3, 2013) Martín García, Germán; Frintrop, Simone; Cremers, Armin B.
    We present an attention-based approach for the detection of unknown objects in a 3D environment. The ability to address individual objects in the environment without having previous knowledge about their properties or their identity is one important requirement of the Situated Vision theory. Based on saliency maps, our attention system determines the regions where objects are likely to be found; these are the proto-objects whose extent is refined by a 2D segmentation step. At the same time a 3D scene model is built from measurements of a depth camera. The detected objects are projected into the 3D scene, resulting in 3D object models which are incrementally updated. We show the validity of our approach in an RGB-D sequence recorded in an office environment.
  • Zeitschriftenartikel
    Attentional Scene-Exploration and Object Discovery in Image and RGB-D Data
    (KI - Künstliche Intelligenz: Vol. 29, No. 1, 2015) Martín García, Germán; Werner, Thomas; Frintrop, Simone
    In this paper, we summarize our project work of the last two years, where we addressed the tasks of visually exploring a scene with visual attention mechanisms based on saliency computation, and of locating unknown objects in the environment. The latter is also called object discovery and consists in finding candidate objects without previous knowledge about the objects themselves or the scene. We follow an approach motivated from human perception and combine saliency and segmentation to generate object candidates. We show results on 2D images as well as on 3D sequences obtained from an RGB-D camera.
  • Zeitschriftenartikel
    Bio-inspired Vision Systems
    (KI - Künstliche Intelligenz: Vol. 29, No. 1, 2015) Frintrop, Simone
  • Zeitschriftenartikel
    Multi-phase Fine-Tuning: A New Fine-Tuning Approach for Sign Language Recognition
    (KI - Künstliche Intelligenz: Vol. 36, No. 1, 2022) Sarhan, Noha; Lauri, Mikko; Frintrop, Simone
    In this paper, we propose multi-phase fine-tuning for tuning deep networks from typical object recognition to sign language recognition (SLR). It extends the successful idea of transfer learning by fine-tuning the network’s weights over several phases. Starting from the top of the network, layers are trained in phases by successively unfreezing layers for training. We apply this novel training approach to SLR, since in this application, training data is scarce and differs considerably from the datasets which are usually used for pre-training. Our experiments show that multi-phase fine-tuning can reach significantly better accuracy in fewer training epochs compared to previous fine-tuning techniques