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Araslanov, Nikita (2022):
Deep Visual Parsing with Limited Supervision. (Publisher's Version)
Darmstadt, Technische Universität Darmstadt,
DOI: 10.26083/tuprints-00022514,
[Ph.D. Thesis]

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Cordts, Marius (2017):
Understanding Cityscapes: Efficient Urban Semantic Scene Understanding.
Darmstadt, Technische Universität,
[Ph.D. Thesis]

H

Hur, Junhwa (2022):
Joint Motion, Semantic Segmentation, Occlusion, and Depth Estimation. (Publisher's Version)
Darmstadt, Technische Universität Darmstadt,
DOI: 10.26083/tuprints-00021624,
[Ph.D. Thesis]

M

Mahajan, Shweta (2022):
Multimodal Representation Learning for Diverse Synthesis with Deep Generative Models. (Publisher's Version)
Darmstadt, Technische Universität Darmstadt,
DOI: 10.26083/tuprints-00021651,
[Ph.D. Thesis]

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Plötz, Tobias (2021):
Measuring and Removing Realistic Image Noise. (Publisher's Version)
Darmstadt, Technische Universität Darmstadt,
DOI: 10.26083/tuprints-00019105,
[Ph.D. Thesis]

R

Rehfeld, Timo (2018):
Combining Appearance, Depth and Motion for Efficient Semantic Scene Understanding.
Darmstadt, Technische Universität,
[Ph.D. Thesis]

Richter, Stephan Randolf (2020):
Visual Perception with Synthetic Data.
Darmstadt, Technische Universität,
DOI: 10.25534/tuprints-00013245,
[Ph.D. Thesis]

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Schelten, Kevin (2018):
Foundations, Inference, and Deconvolution in Image Restoration.
Darmstadt, Technische Universität,
[Ph.D. Thesis]

Schmidt, Uwe (2017):
Half-quadratic Inference and Learning for Natural Images.
Darmstadt, Technische Universität,
[Ph.D. Thesis]

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Wannenwetsch, Anne Sabine (2021):
Probabilistic Optical Flow and its Image-Adaptive Refinement. (Publisher's Version)
Darmstadt, Technische Universität,
DOI: 10.26083/tuprints-00019455,
[Ph.D. Thesis]