At a glance
- Opportunity
- Master's thesis
- Location
- Darmstadt, DE, 64295
- Funding / pay
- Not specified by the organization
- Eligibility
- Check the qualifications in the official posting
Project description
Background/Motivation: Models that can recognise human skin, body parts, or scenes are often used to detect erotic and pornographic material. With the help of appropriate datasets [1], classification and object detection models can be trained. However, there are also images that are obviously erotic or pornographic, but cannot be recognised by conventional methods. This applies, for example, to people in skin-tight latex or leather clothing. Existing approaches in the field of "Human Parsing" can already segment people and their clothing well. Additionally, datasets like Fashionpedia [2] exist, which include segmentation masks and labels for clothing items.Objective: The aim of this master's thesis is to investigate whether and to what extent clothing items can be used for the recognition of erotic and pornographic imagery. First, it should be researched which existing approaches are suitable for addressing the question. Gaps in existing datasets and models should be described and filled with our own data and models. Based on the developed methods, it should then be evaluated whether (1) reliable detection of erotic clothing is possible and (2) whether erotic and pornographic images can be distinguished from other categories based on the recognised clothing. In this context, different counter classes should be evaluated, such as everyday, sports, or beach images.Results: As part of this master's thesis, the following results are to be achieved: Dataset with annotations for the detection of erotic clothing. Implementation of new approaches for the detection and segmentation of erotic clothing items. Classification of the detected garments. Evaluation of the models, both in terms of object detection/segmentation and classification (pornographic/erotic/normal). Be part of change Building a dataset for object detection or segmentation. Use of pre-trained state-of-the-art models like SAM 3 to generate annotations. Training models like YOLO, RT-DETR, Mask R-CNN. Analysis of existing datasets regarding the clothing present. Evaluation of the trained models on suitable datasets.
Funding and compensation
Compensation not specified. See the original posting for amounts, duration and conditions.
Eligibility and application requirements
Review the qualifications, research interests and required documents in the description above. The employer or university's original posting is the source for complete eligibility requirements.
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Submit your application through Fraunhofer's official application process. Apply early as a closing date has not been specified.
Source and listing information
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