We tackle the problem of discovering novel classes in an image collection given labelled
examples of other classes. We present a new approach called AutoNovel to address this …

This paper tackles the problem of learning a finer representation than the one provided by
training labels. This enables fine-grained category retrieval of images in a collection …

This paper concerns open-world classification, where the classifier not only needs to classify
test examples into seen classes that have appeared in training but also reject examples from …

We tackle the problem of novel class discovery and localization (NCDL). In this setting, we
assume a source dataset with supervision for only some object classes. Instances of other …

We tackle the challenging task of unsupervised object localization in this work. Recently,
transformers trained with self-supervised learning have been shown to exhibit object …

We tackle object category discovery, which is the problem of discovering and localizing
novel objects in a large unlabeled dataset. While existing methods show results on datasets …

This paper addresses the problem of object discovery from unlabeled driving videos
captured in a realistic automotive setting. Identifying recurring object categories in such raw …

We explore object discovery and detector adaptation based on unlabeled video sequences
captured from a mobile platform. We propose a fully automatic approach for object mining …

Recent work has shown that convolutional neural networks (CNNs) trained in a supervised
fashion for speaker identification are able to extract features from spectrograms which can …

Object detectors are typically trained once and for all on a fixed set of classes. However, this
closed-world assumption is unrealistic in practice, as new classes will inevitably emerge …