Additionally, in heterogenous FL, each local client may only have access to a subset of label space (referred to as openset label learning), meanwhile without overlapping with others. In this work, we study the challenge of FL with local openset noisy labels. We observe that many existing ...
Additionally, in heterogenous FL, each local client may only have access to a subset of label space (referred to as openset label learning), meanwhile without overlapping with others. In this work, we study the challenge of FL with local openset noisy labels. We observe that many existing ...
Through the training with the IMPM loss, we can find that each modality’s samples achieve effective clustering, but the cross-modal domain gap still exists. Furthermore, through training with the CMPM loss, the feature distributions of the two modalities are highly overlapped, but a few ...
In clustering by fast search and find of density peaks (CDP)4, cluster centers are characterized as points with higher local density and having large distance from any other local density. CDP uses a decision graph based approach to identify cluster centers, in a more intuitive way as compared...
In [39], a unified probabilistic method was proposed to perform global and local feature selection for clustering. An embedded method was developed in [40] to locally weight variables for global feature selection. These methods mainly explore the local information to select feature subsets. In ...
To fulfill data mining tasks, feature selection is usually followed by classification or clustering to reveal the intrinsic data structure. Although a few classification methods such as support vector machine (SVM) [16] could achieve the task of feature selection simultaneously, they are usually perfo...
In Table 6 (top), we show the performance of our network f trained with different viewpoint selection rules in multi-view rendering. Concretely, the straightfor- ward random sampling rule places the viewpoints randomly within the range in Eq. 5. The viewpoi...
Secondly, we constructed a label propagation technique integrated with the adaptive graph learning in SFS-AGGL to fully utilize the structural distribution information of both labeled and unlabeled data. The proposed SFS-AGGL method is validated through classification and clustering tasks across various ...
Moving object detection for event-based vision using graph spectral clustering. In Proceedings of the IEEE/CVF International Conference on Computer Vision, Montreal, BC, Canada, 11–17 October 2021; pp. 876–884. [Google Scholar] Giraldo, J.H.; Javed, S.; Werghi, N.; Bouwmans, T. ...
This module integrates global context modeling to create long-range dependencies and local interactions to enable channel attention ability by using 1D convolution that not only performs well with noisy labels but also consumes significantly less resources without any dimensionality reduction. The module ...