Olga Moskvyak

Olga Moskvyak

Machine Learning Scientist, PhD in Computer Science

Publications

Learning from limited annotated data for re-identification problem

Olga Moskvyak, PhD Thesis (2021). "Learning from limited annotated data for re-identification problem."

Going Deeper into Semi-supervised Person Re-identification

Olga Moskvyak, Frederic Maire, Feras Dayoub & Mahsa Baktashmotlagh (2021). "Going Deeper into Semi-supervised Person Re-identification." Arxiv preprint, In Review.

Semi-supervised keypoint localization

Olga Moskvyak, Frederic Maire, Feras Dayoub & Mahsa Baktashmotlagh (2021). "Semi-supervised keypoint localization." In Proc. International Conference on Learning Representations (ICLR).

  • ICLR is a top-3 publication venue in Engineering and Computer Science and top-17 among all scientific publication venues with h-index 203 (203 ICLR papers have gathered more than 203 citations in the last 5 years). ICLR is a top-tier conference with CORE rating A*.

Keypoint-aligned embeddings for image retrieval and re-identification

Olga Moskvyak, Frederic Maire, Feras Dayoub, Mahsa Baktashmotlagh. (2021). "Keypoint-aligned embeddings for image retrieval and re-identification." In Proc. Winter Conference on Applications of Computer Vision (WACV). pp. 676-685.

  • WACV is the premier international computer vision conference with rating A.

Learning Landmark Guided Embeddings for Animal Re-identification

Olga Moskvyak, Frederic Maire, Feras Dayoub and Mahsa Baktashmotlagh. (2020). "Learning Landmark Guided Embeddings for Animal Re-identification." In Proc. Winter Conference on Applications of Computer Vision Workshops

  • WACV is the premier international computer vision conference with rating A.

Robust re-identification of manta rays from natural markings by learning pose invariant embeddings

Olga Moskvyak, Frederic Maire, Asia O Armstrong, Feras Dayoub & Mahsa Baktashmotlagh (2021). "Robust re-identification of manta rays from natural markings by learning pose invariant embeddings." In Proc. International Conference on Digital Image Computing: Techniques and Applications (DICTA).

Learning geometric equivalence between patterns using embedding neural networks

Olga Moskvyak & Frederic Maire (2017). "Learning geometric equivalence between patterns using embedding neural networks." In Proc. International Conference on Digital Image Computing: Techniques and Applications (DICTA).