courses:wshop:topics:tematy2023zima

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courses:wshop:topics:tematy2023zima [2023/10/06 13:59] – [[JKO] Template] jeremicourses:wshop:topics:tematy2023zima [2023/10/13 12:37] (current) – [[KKT] Support in BIRAFFE3 experiment] kkt
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 ==== [KKT] FACE APIs comparison - follow-up study ==== ==== [KKT] FACE APIs comparison - follow-up study ====
  
-  * **Student:** FIXME +  * **Student:** Michał Przysucha 
-  * **Namespace in the wiki:** [[..:projects:2023:FIXME:]]+  * **Namespace in the wiki:** [[..:projects:2023:faceapis-follow:]]
   * **The goal of the project:** Comparison of the effectiveness of off-the-shelf APIs and pre-trained models for emotion recognition in non-trivial images   * **The goal of the project:** Comparison of the effectiveness of off-the-shelf APIs and pre-trained models for emotion recognition in non-trivial images
   * **Technology:** Python, data analysis   * **Technology:** Python, data analysis
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 ==== [KKT] Affect changes as probability blobs (with RegFlow) in Valence x Arousal space ==== ==== [KKT] Affect changes as probability blobs (with RegFlow) in Valence x Arousal space ====
  
-  * **Student:** FIXME +  * **Student:** Konrad Micek 
-  * **Namespace in the wiki:** [[..:projects:2023:FIXME:]]+  * **Namespace in the wiki:** [[..:projects:2023:regflow:]]
   * **The goal of the project:** Adapt RegFlow method to 2-D emotion prediction task   * **The goal of the project:** Adapt RegFlow method to 2-D emotion prediction task
   * **Technology:** Python, data analysis   * **Technology:** Python, data analysis
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 ==== [KKT] Support in BIRAFFE3 experiment ==== ==== [KKT] Support in BIRAFFE3 experiment ====
  
-  * **Student:** FIXME +  * **Student:** Honorata Zych 
-  * **Namespace in the wiki:** [[..:projects:2023:FIXME:]]+  * **Namespace in the wiki:** [[..:projects:2023:bir3support:]]
   * **The goal of the project:** Support in BIRAFFE3 experiment preparation (pilot data analysis) and then collaboration with actual experiment   * **The goal of the project:** Support in BIRAFFE3 experiment preparation (pilot data analysis) and then collaboration with actual experiment
   * **Technology:** Python, data analysis   * **Technology:** Python, data analysis
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 ==== [KKT] Emotion recognition for everyday life - evaluation of the state-of-the-art ==== ==== [KKT] Emotion recognition for everyday life - evaluation of the state-of-the-art ====
  
-  * **Student:** FIXME +  * **Student:** Anastasiya Yurenia 
-  * **Namespace in the wiki:** [[..:projects:2023:FIXME:]]+  * **Namespace in the wiki:** [[..:projects:2023:emognition:]]
   * **The goal of the project:** Replicate and evaluate methods and tools proposed for emotion recognition by [[https://emognition.com/|Emognition]] team from PWr   * **The goal of the project:** Replicate and evaluate methods and tools proposed for emotion recognition by [[https://emognition.com/|Emognition]] team from PWr
   * **Technology:** reading :), Python, data analysis, machine learning   * **Technology:** reading :), Python, data analysis, machine learning
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-==== [KKT] Loki on the triplestore ====+==== [KKT] Loki on a triplestore ====
  
-  * **Student:** FIXME +  * **Student:** Dominik Tyszownicki 
-  * **Namespace in the wiki:** [[..:projects:2023:FIXME:]]+  * **Namespace in the wiki:** [[..:projects:2023:loki:]]
   * **The goal of the project:** Get out of the SWI-Prolog from the Loki. Review current graph bases engines (triplestores), select the most promising one and move the whole knowledge to the selected triplestore.​   * **The goal of the project:** Get out of the SWI-Prolog from the Loki. Review current graph bases engines (triplestores), select the most promising one and move the whole knowledge to the selected triplestore.​
   * **Technology:** PHP, Semantic Web   * **Technology:** PHP, Semantic Web
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   * [[https://arxiv.org/abs/1806.02786|Sun, S., Yeh, C.-F., Hwang, M.-Y., Ostendorf, M. & Xie, L. Domain Adversarial Training for Accented Speech Recognition. in 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) 4854–4858 (2018).]]   * [[https://arxiv.org/abs/1806.02786|Sun, S., Yeh, C.-F., Hwang, M.-Y., Ostendorf, M. & Xie, L. Domain Adversarial Training for Accented Speech Recognition. in 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) 4854–4858 (2018).]]
   * [[https://accent.gmu.edu/|The speech accent archive]]   * [[https://accent.gmu.edu/|The speech accent archive]]
 +
 +==== [JKO] Physics informed neural networks (PINN) for object tracking in sports ====
 +
 +  * **Student:** FIXME
 +  * **Namespace in the wiki:**  [[..:projects:2023:FIXME:]]
 +  * **The goal of the project:** Paper review and adjusting a chosen PINN for single object traking. 
 +  * **Technology:** Python, Julia
 +  * **Description:** The ultimate goal (larger than this project) is to create a model to infer the movement of table tennis players to analyse their gameplay from single camera videos. One has to merge (a) pose estimation models (including a detailed hand position estimation) with (b) ball trajectory tracking and informing (a) with the physical parameters inferred from (b). The challenge is low sampling (only a couple of frames per one ball shot), blurr, camera angles make depth estimation hard, etc. There are a number of existing implementations of neural networks that explicitly incorporate physical equations/quantities. The goal it to find a suitable one, scrap a small amount of data (we can actually record high quality data ourselves), and try it out. Possibly there are Julia and Python alternatives to be considered.
 +  * **Links (as a starting point):**
 +  * [[https://arxiv.org/abs/1907.07587|A Differentiable Programming System to Bridge Machine Learning and Scientific Computing.]]
 +  * [[https://arxiv.org/abs/2211.07377|Physics-Guided, Physics-Informed, and Physics-Encoded Neural Networks in Scientific Computing]]
 +
 ==== [FIXME] Template ==== ==== [FIXME] Template ====
  
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