84 citations · 339 across the 34 of their papers we have counts for
7 papers · 1 filter
Few-shot Weakly-Supervised Object Detection via Directional Statistics
Amirreza Shaban, Amir Rahimi, Thalaiyasingam Ajanthan +2
Detecting novel objects from few examples has become an emerging topic in computer vision recently. However, these methods need fully annotated training images to learn new object…
Combining pretrained CNN feature extractors to enhance clustering of complex natural images
Joris Guerin, Stephane Thiery, Eric Nyiri +2
Recently, a common starting point for solving complex unsupervised image classification tasks is to use generic features, extracted with deep Convolutional Neural Networks (CNN) pr…
Pairwise Similarity Knowledge Transfer for Weakly Supervised Object Localization
Amir Rahimi, Amirreza Shaban, Thalaiyasingam Ajanthan +2
Weakly Supervised Object Localization (WSOL) methods only require image level labels as opposed to expensive bounding box annotations required by fully supervised algorithms. We st…
Learning to Find Common Objects Across Few Image Collections
Amirreza Shaban, Amir Rahimi, Shray Bansal +3
Given a collection of bags where each bag is a set of images, our goal is to select one image from each bag such that the selected images are from the same object class. We model t…
Learning to Align Images using Weak Geometric Supervision
Jing Dong, Byron Boots, Frank Dellaert +2
Image alignment tasks require accurate pixel correspondences, which are usually recovered by matching local feature descriptors. Such descriptors are often derived using supervised…
Improving Image Clustering With Multiple Pretrained CNN Feature Extractors
Joris Guérin, Byron Boots
For many image clustering problems, replacing raw image data with features extracted by a pretrained convolutional neural network (CNN), leads to better clustering performance. How…