no code implementations • 9 Aug 2023 • André Peter Kelm, Niels Hannemann, Bruno Heberle, Lucas Schmidt, Tim Rolff, Christian Wilms, Ehsan Yaghoubi, Simone Frintrop
Our proposed topology also comes with a built-in top-down attention mechanism, which allows processing to be directly influenced by either enhancing or inhibiting category-specific high-level features, drawing parallels to the selective attention mechanism observed in human cognition.
1 code implementation • 21 Oct 2021 • Bruno Degardin, João Neves, Vasco Lopes, João Brito, Ehsan Yaghoubi, Hugo Proença
Synthesising the spatial and temporal dynamics of the human body skeleton remains a challenging task, not only in terms of the quality of the generated shapes, but also of their diversity, particularly to synthesise realistic body movements of a specific action (action conditioning).
Ranked #1 on Human action generation on Human3.6M
1 code implementation • 19 Jun 2020 • S. V. Aruna Kumar, Ehsan Yaghoubi, Hugo Proença
In video-based person re-identification, both the spatial and temporal features are known to provide orthogonal cues to effective representations.
1 code implementation • 6 Apr 2020 • S. V. Aruna Kumar, Ehsan Yaghoubi, Abhijit Das, B. S. Harish, Hugo Proença
Over the last decades, the world has been witnessing growing threats to the security in urban spaces, which has augmented the relevance given to visual surveillance solutions able to detect, track and identify persons of interest in crowds.
1 code implementation • 2 Apr 2020 • Ehsan Yaghoubi, Diana Borza, João Neves, Aruna Kumar, Hugo Proença
The automatic characterization of pedestrians in surveillance footage is a tough challenge, particularly when the data is extremely diverse with cluttered backgrounds, and subjects are captured from varying distances, under multiple poses, with partial occlusion.
1 code implementation • 26 Feb 2020 • Hugo Proença, Ehsan Yaghoubi, Pendar Alirezazadeh
This paper describes one objective function for learning semantically coherent feature embeddings in multi-output classification problems, i. e., when the response variables have dimension higher than one.
1 code implementation • 30 Jan 2020 • Ehsan Yaghoubi, Diana Borza, Aruna Kumar, Hugo Proença
The \emph{receptive fields} of deep learning classification models determine the regions of the input data that have the most significance for providing correct decisions.