Hard negative mixing for contrastive learning
Contrastive learning has become a key component of self-supervised learning approaches
for computer vision. By learning to embed two augmented versions of the same image close …
for computer vision. By learning to embed two augmented versions of the same image close …
A unified objective for novel class discovery
In this paper, we study the problem of Novel Class Discovery (NCD). NCD aims at inferring
novel object categories in an unlabeled set by leveraging from prior knowledge of a labeled …
novel object categories in an unlabeled set by leveraging from prior knowledge of a labeled …
Learning semi-supervised gaussian mixture models for generalized category discovery
In this paper, we address the problem of generalized category discovery (GCD), ie, given a
set of images where part of them are labelled and the rest are not, the task is to automatically …
set of images where part of them are labelled and the rest are not, the task is to automatically …
Neighborhood contrastive learning for novel class discovery
In this paper, we address Novel Class Discovery (NCD), the task of unveiling new classes in
a set of unlabeled samples given a labeled dataset with known classes. We exploit the …
a set of unlabeled samples given a labeled dataset with known classes. We exploit the …
Cross-domain adaptive clustering for semi-supervised domain adaptation
In semi-supervised domain adaptation, a few labeled samples per class in the target domain
guide features of the remaining target samples to aggregate around them. However, the …
guide features of the remaining target samples to aggregate around them. However, the …
Autonovel: Automatically discovering and learning novel visual categories
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 …
examples of other classes. We present a new approach called AutoNovel to address this …
Novel visual category discovery with dual ranking statistics and mutual knowledge distillation
In this paper, we tackle the problem of novel visual category discovery, ie, grou**
unlabelled images from new classes into different semantic partitions by leveraging a …
unlabelled images from new classes into different semantic partitions by leveraging a …
Interactiveness field in human-object interactions
Abstract Human-Object Interaction (HOI) detection plays a core role in activity
understanding. Though recent two/one-stage methods have achieved impressive results, as …
understanding. Though recent two/one-stage methods have achieved impressive results, as …
Joint representation learning and novel category discovery on single-and multi-modal data
This paper studies the problem of novel category discovery on single-and multi-modal data
with labels from different but relevant categories. We present a generic, end-to-end …
with labels from different but relevant categories. We present a generic, end-to-end …
Adaptive betweenness clustering for semi-supervised domain adaptation
Compared to unsupervised domain adaptation, semi-supervised domain adaptation (SSDA)
aims to significantly improve the classification performance and generalization capability of …
aims to significantly improve the classification performance and generalization capability of …