.png){kind=icon}
.png){kind=icon}
Return
Learning Dynamic Batch-Graph Representation for Deep Representation Learning
.png){kind=icon}
PRE
AI
DOI：10.1007/s11263-024-02175-8.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Recently, batch-based image data representation has been demonstrated to be effective for context-enhanced image representation. The core issue for this task is capturing the dependences of image samples within each mini-batch and conducting message communication among different samples. Existing approaches mainly adopt self-attention or local self-attention models (on patch dimension) for this task which fail to fully exploit the intrinsic relationships of samples within mini-batch and also be sensitive to noises and outliers. To address this issue, in this paper, we propose a flexible Dynamic Batch-Graph Representation (DyBGR) model, to automatically explore the intrinsic relationship of samples for contextual sample representation. Specifically, DyBGR first represents the mini-batch with a graph (termed batch-graph) in which nodes represent image samples and edges encode the dependences of images. This graph is dynamically learned with the constraint of similarity, sparseness and semantic correlation. Upon this, DyBGR exchanges the sample (node) information on the batch-graph to update each node representation. Note that, both batch-graph learning and information propagation are jointly optimized to boost their respective performance. Furthermore, in practical, DyBGR model can be implemented via a simple plug-and-play block (named DyBGR block) which thus can be potentially integrated into any mini-batch based deep representation learning schemes. Extensive experiments on deep metric learning tasks demonstrate the effectiveness of DyBGR. We will release the code at https://github.com/SissiW/DyBGR.
Keywords:
Image representation learning
Transformer
Graph learning
Metric learning
Mini-batch
Journal
IF:
9.3
Papers:
3.9K
Citations:
2.8W