18 citations · 61 across the 7 of their papers we have counts for
7 papers
HYperbolic Self-Paced Learning for Self-Supervised Skeleton-based Action Representations
Luca Franco, Paolo Mandica, Bharti Munjal +1
Self-paced learning has been beneficial for tasks where some initial knowledge is available, such as weakly supervised learning and domain adaptation, to select and order the train…
Are we certain it's anomalous?
Alessandro Flaborea, Bardh Prenkaj, Bharti Munjal +4
The progress in modelling time series and, more generally, sequences of structured data has recently revamped research in anomaly detection. The task stands for identifying abnorma…
Query-Guided Networks for Few-shot Fine-grained Classification and Person Search
Bharti Munjal, Alessandro Flaborea, Sikandar Amin +2
Few-shot fine-grained classification and person search appear as distinct tasks and literature has treated them separately. But a closer look unveils important similarities: both t…
Class Interference Regularization
Bharti Munjal, Sikandar Amin, Fabio Galasso
Contrastive losses yield state-of-the-art performance for person re-identification, face verification and few shot learning. They have recently outperformed the cross-entropy loss…
Joint Detection and Tracking in Videos with Identification Features
Bharti Munjal, Abdul Rafey Aftab, Sikandar Amin +3
Recent works have shown that combining object detection and tracking tasks, in the case of video data, results in higher performance for both tasks, but they require a high frame-r…
Knowledge Distillation for End-to-End Person Search
Bharti Munjal, Fabio Galasso, Sikandar Amin
We introduce knowledge distillation for end-to-end person search. End-to-End methods are the current state-of-the-art for person search that solve both detection and re-identificat…
Query-guided End-to-End Person Search
Bharti Munjal, Sikandar Amin, Federico Tombari +1
Person search has recently gained attention as the novel task of finding a person, provided as a cropped sample, from a gallery of non-cropped images, whereby several other people…