Corpus ID: 195346999

Transferring Common-Sense Knowledge for Object Detection

  title={Transferring Common-Sense Knowledge for Object Detection},
  author={Krishna Kumar Singh and S. Divvala and Ali Farhadi and Yong Jae Lee},
We propose the idea of transferring common-sense knowledge from source categories to target categories for scalable object detection. [...] Key Method We acquire such common-sense cues automatically from readily-available knowledge bases without any extra human effort. On the challenging MS COCO dataset, we find that using common-sense knowledge substantially improves detection performance over existing transfer-learning baselines.Expand
Analysing object detectors from the perspective of co-occurring object categories
  • Csaba Nemes, Sándor Jordán
  • Computer Science
  • 2018 9th IEEE International Conference on Cognitive Infocommunications (CogInfoCom)
  • 2018
According to measurements, current detectors usually do not build strong dependency on contextual information at category level, however, when they does, they does it in a similar way, suggesting that contextual dependence of object categories is an independent property that is relevant to be transferred. Expand


Large Scale Semi-Supervised Object Detection Using Visual and Semantic Knowledge Transfer
Strong evidence is found that visual similarity and semantic relatedness are complementary for the task, and when combined notably improve detection, achieving state-of-the-art detection performance in a semi-supervised setting. Expand
Object-Centric Spatial Pooling for Image Classification
A framework that learns object detectors using only image-level class labels, or so-called weak labels is proposed, comparable in accuracy with state-of-the-art weakly supervised detection methods and significantly outperforms SPM-based pooling in image classification. Expand
Object-Graphs for Context-Aware Visual Category Discovery
  • Yong Jae Lee, K. Grauman
  • Computer Science, Medicine
  • IEEE Transactions on Pattern Analysis and Machine Intelligence
  • 2012
This work introduces two variants of a novel object-graph descriptor to encode the 2D and 3D spatial layout of object-level co--occurrence patterns relative to an unfamiliar region and shows that by using them to model the interaction between an image's known and unknown objects, it can better detect new visual categories. Expand
Object Detection Meets Knowledge Graphs
A novel framework of knowledge-aware object detection is proposed, which enables the integration of external knowledge such as knowledge graphs into any object detection algorithm, which improves object detection through a re-optimization process to achieve better consistency with background knowledge. Expand
Object Classification with Adaptable Regions
This paper proposes a new latent SVM model for category level object classification that combines spatial and co-occurrence relations between adjacent regions, such that unlikely configurations are penalized and semantic representation can be exploited for finding similar content. Expand
Microsoft COCO: Common Objects in Context
We present a new dataset with the goal of advancing the state-of-the-art in object recognition by placing the question of object recognition in the context of the broader question of sceneExpand
Transfer Learning by Ranking for Weakly Supervised Object Annotation
A novel transfer learning based on learning to rank is formulated, which effectively transfers a model for automatic annotation of object location from an auxiliary dataset to a target dataset with completely unrelated object categories. Expand
LSDA: Large Scale Detection through Adaptation
This paper proposes Large Scale Detection through Adaptation (LSDA), an algorithm which learns the difference between the two tasks and transfers this knowledge to classifiers for categories without bounding box annotated data, turning them into detectors. Expand
The Role of Context for Object Detection and Semantic Segmentation in the Wild
A novel deformable part-based model is proposed, which exploits both local context around each candidate detection as well as global context at the level of the scene, which significantly helps in detecting objects at all scales. Expand
Semi-supervised Domain Adaptation with Instance Constraints
It is shown that imposing smoothness constraints on the classifier scores over the unlabeled data can lead to improved adaptation results, and this work proposes techniques that build on existing domain adaptation methods by explicitly modeling these relationships, and demonstrates empirically that they improve recognition accuracy. Expand