Based on this, a new multinetwork mean distillation loss function for open-world domain incremental object detection is presented. To better extract reliable and stable knowledge from old models, we enhanced the distillation output of the detector with a ResNet50 backbone and an ...
To address this issue, we propose a novel multi-domain adaptation method for object detection based on incremental learning. Specifically, the incremental learning network saves the knowledge of multiple domains and makes the model to fuse the knowledge of different domains during the training ...
Amazon SageMaker JumpStart models and algorithms now available via API Incremental training with Amazon SageMaker JumpStart Transfer learning for TensorFlow object detection models in Amazon SageMaker Transfer learning for TensorFlow text classification models in Amazon SageMake...
IncrementalPullConfig IntegrationConfig JobSchedule JobStats ListCalculatedAttributeDefinitionItem ListCalculatedAttributeForProfileItem ListDomainItem ListIntegrationItem ListObjectTypeAttributeItem ListProfileObjectsItem ListProfileObjectTypeItem ListProfileObjectTypeTemplateItem ListWorkflowsItem MarketoSourceProperties Mat...
Incremental few-shot object detection aims at detecting novel classes without forgetting knowledge of the base classes with only a few labeled training data from the novel classes. Most related prior works are on incremental object detec... N Dong,Y Zhang,M Ding,... - 《Arxiv》 被引量: 0...
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There is a vital need in the horticulture research field to understand fruit-related phenotypic traits, such as fruit number, size, and color. With the rapid development of modern computer technology, the demand for visual detection techniques in agriculture has increased. An object detection techniq...
The model should be also developed in an incremental form to update with sequentially-arrived data. Moreover, adaptive extraction of anomalous pattern, even the unseen pattern, is another interesting problem in the study of one-class transfer learning. Appendix The objective function in (6) has...
3 shows the effects of incremental addition of sub- networks to estimate defocus maps from synthetic (upper row) and real (lower row) images. Given a synthetic image, DMENetB estimates a defocus map reasonably well. How- ever, for a real defocused image, the sole use of subnetwor...
Class-Incremental Domain Adaptation Spatial Attention Pyramid Network for Unsupervised Domain Adaptation CSCL: Critical Semantic-Consistent Learning for Unsupervised Domain Adaptation Partially-Shared Variational Auto-encoders for Unsupervised Domain Adaptation with Target Shift ...