English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Training of object detection models using less data is currently the focus ofexisting N-shot learning models in computer vision. Such methods useobject-level labels and takes hours to train on unseen classes. There are manycases where we have large amount of image-level labels available for trainingbut cannot be utilized by few shot object detection models for training. Thereis a need for a machine learning framework that can be used for training anyunseen class and can become useful in real-time situations. In this paper, weproposed an "Unseen Class Detector" that can be trained within a very shorttime for any possible unseen class without bounding boxes with competitiveaccuracy. We build our approach on "Strong" and "Weak" baseline detectors,which we trained on existing object detection and image classificationdatasets, respectively. Unseen concepts are fine-tuned on the strong baselinedetector using only image-level labels and further adapted by transferring theclassifier-detector knowledge between baselines. We use semantic as well asvisual similarities to identify the source class (i.e. Sheep) for thefine-tuning and adaptation of unseen class (i.e. Goat). Our model (UnseenNet)is trained on the ImageNet classification dataset for unseen classes and testedon an object detection dataset (OpenImages). UnseenNet improves the meanaverage precision (mAP) by 10% to 30% over existing baselines (semi-supervisedand few-shot) of object detection on different unseen class splits. Moreover,training time of our model is <10 min for each unseen class. Qualitativeresults demonstrate that UnseenNet is suitable not only for few classes ofPascal VOC but for unseen classes of any dataset or web. Code is available athttps://github.com/Asra-Aslam/UnseenNet.

UnseenNet: Fast Training Detector for Any Unseen Concept