7 citations · 13 across the 5 of their papers we have counts for
4 papers · 1 filter
Leveraging Transformers for Weakly Supervised Object Localization in Unconstrained Videos
Shakeeb Murtaza, Marco Pedersoli, Aydin Sarraf +1
Weakly-Supervised Video Object Localization (WSVOL) involves localizing an object in videos using only video-level labels, also referred to as tags. State-of-the-art WSVOL methods…
DiPS: Discriminative Pseudo-Label Sampling with Self-Supervised Transformers for Weakly Supervised Object Localization
Shakeeb Murtaza, Soufiane Belharbi, Marco Pedersoli +2
Self-supervised vision transformers (SSTs) have shown great potential to yield rich localization maps that highlight different objects in an image. However, these maps remain class…
Constrained Sampling for Class-Agnostic Weakly Supervised Object Localization
Shakeeb Murtaza, Soufiane Belharbi, Marco Pedersoli +2
Self-supervised vision transformers can generate accurate localization maps of the objects in an image. However, since they decompose the scene into multiple maps containing variou…
F-CAM: Full Resolution Class Activation Maps via Guided Parametric Upscaling
Soufiane Belharbi, Aydin Sarraf, Marco Pedersoli +3
Class Activation Mapping (CAM) methods have recently gained much attention for weakly-supervised object localization (WSOL) tasks. They allow for CNN visualization and interpretati…