Universal-RCNN: Universal Object Detector via Transferable Graph R-CNN

by   Hang Xu, et al.

The dominant object detection approaches treat each dataset separately and fit towards a specific domain, which cannot adapt to other domains without extensive retraining. In this paper, we address the problem of designing a universal object detection model that exploits diverse category granularity from multiple domains and predict all kinds of categories in one system. Existing works treat this problem by integrating multiple detection branches upon one shared backbone network. However, this paradigm overlooks the crucial semantic correlations between multiple domains, such as categories hierarchy, visual similarity, and linguistic relationship. To address these drawbacks, we present a novel universal object detector called Universal-RCNN that incorporates graph transfer learning for propagating relevant semantic information across multiple datasets to reach semantic coherency. Specifically, we first generate a global semantic pool by integrating all high-level semantic representation of all the categories. Then an Intra-Domain Reasoning Module learns and propagates the sparse graph representation within one dataset guided by a spatial-aware GCN. Finally, an InterDomain Transfer Module is proposed to exploit diverse transfer dependencies across all domains and enhance the regional feature representation by attending and transferring semantic contexts globally. Extensive experiments demonstrate that the proposed method significantly outperforms multiple-branch models and achieves the state-of-the-art results on multiple object detection benchmarks (mAP: 49.1 COCO).


Graphonomy: Universal Human Parsing via Graph Transfer Learning

Prior highly-tuned human parsing models tend to fit towards each dataset...

Hybrid Knowledge Routed Modules for Large-scale Object Detection

The dominant object detection approaches treat the recognition of each r...

Learning Finer-class Networks for Universal Representations

Many real-world visual recognition use-cases can not directly benefit fr...

Graphonomy: Universal Image Parsing via Graph Reasoning and Transfer

Prior highly-tuned image parsing models are usually studied in a certain...

SGNet: A Super-class Guided Network for Image Classification and Object Detection

Most classification models treat different object classes in parallel an...

Panoptic Edge Detection

Pursuing more complete and coherent scene understanding towards realisti...

Window-Object Relationship Guided Representation Learning for Generic Object Detections

In existing works that learn representation for object detection, the re...

Please sign up or login with your details

Forgot password? Click here to reset