The Pursuit of Knowledge: Discovering and Localizing Novel Categories using Dual Memory
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 with less cluttered scenes and fewer object instances per image, we present our results on the challenging COCO dataset. Moreover, we argue that, rather than discovering new categories from scratch, discovery algorithms can benefit from identifying what is already known and focusing their attention on the unknown. We propose a method that exploits prior knowledge about certain object types to discover new categories by leveraging two memory modules, namely Working and Semantic memory. We show the performance of our detector on the COCO minival dataset to demonstrate its in-the-wild capabilities.
Code (0)
등록된 구현이 없습니다.
Tasks
ObjectSimilar Papers 제목 키워드 기반
Beat-Event Detection in Action Movie Franchises
While important advances were recently made towards temporally localizing and recognizing specific human actions or activities in videos, efficient detection and classification of long video chunks belonging to semantica…
ClassificationEvent DetectionGeneral ClassificationSemi-supervised model-based clustering with controlled clusters leakage
In this paper, we focus on finding clusters in partially categorized data sets. We propose a semi-supervised version of Gaussian mixture model, called C3L, which retrieves natural subgroups of given categories. In contra…
ClusteringProxy Anchor-based Unsupervised Learning for Continuous Generalized Category Discovery
Recent advances in deep learning have significantly improved the performance of various computer vision applications. However, discovering novel categories in an incremental learning scenario remains a challenging proble…
class-incremental learningClass Incremental LearningIncremental Learningunsupervised class-incremental learningNovel Visual Category Discovery with Dual Ranking Statistics and Mutual Knowledge Distillation
In this paper, we tackle the problem of novel visual category discovery, i.e., grouping unlabelled images from new classes into different semantic partitions by leveraging a labelled dataset that contains images from oth…
Fine-Grained Visual RecognitionKnowledge DistillationClass-incremental Novel Class Discovery
We study the new task of class-incremental Novel Class Discovery (class-iNCD), which refers to the problem of discovering novel categories in an unlabelled data set by leveraging a pre-trained model that has been trained…
Incremental LearningKnowledge DistillationNovel Class Discovery