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Now showing items 1-5 of 5

    • Occlusion-Ordered Semantic Instance Segmentation 

      Baselizadeh, Soroosh (University of Waterloo, 2023-08-23)
      Conventional semantic ‘instance’ segmentation methods offer a segmentation mask for each object instance in an image along with its semantic class label. These methods excel in distinguishing instances, whether they belong ...
    • Self-supervised Video Representation Learning by Exploiting Video Speed Changes 

      Chen, Lizhe (University of Waterloo, 2022-04-29)
      In recent research, the self-supervised video representation learning methods have achieved improvement by exploring video’s temporal properties, such as playing speeds and temporal order. These works inspire us to exploit ...
    • Test-Time Training for Image Inpainting 

      Ghiro, Genseric (University of Waterloo, 2022-06-21)
      Image inpainting is the task of filling missing regions in images with plausible and coherent content. The usual process involves training a CNN on a large collection of examples that it can learn from, to later apply this ...
    • Volumetric Weak Supervision for Semantic Segmentation 

      Bashar, Sharhad (University of Waterloo, 2022-05-24)
      Semantic segmentation is a popular task in computer vision. Fully supervised methods are data hungry, they require pixel precise annotations for thousands of images. To reduce user annotation efforts, weak supervision for ...
    • Weakly-supervised Semantic Segmentation with Regularized Loss Hyperparameter Search 

      Ji, Zongliang (University of Waterloo, 2021-09-20)
      Weakly supervised segmentation signi cantly reduces user annotation e ort. Recently, regularized loss was proposed for single object class segmentation under image-level weak supervision. Regularized loss consists of ...


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