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/

Zendy Plus

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

Zendy Tools

Zendy Open

An examination of object recognition challenge leaderboards (ILSVRC,PASCAL-VOC) reveals that the top-performing classifiers typically exhibit smalldifferences amongst themselves in terms of error rate/mAP. To betterdifferentiate the top performers, additional criteria are required. Moreover,the (test) images, on which the performance scores are based, predominantlycontain fully visible objects. Therefore, `harder' test images, mimicking thechallenging conditions (e.g. occlusion) in which humans routinely recognizeobjects, need to be utilized for benchmarking. To address the concernsmentioned above, we make two contributions. First, we systematically vary thelevel of local object-part content, global detail and spatial context in imagesfrom PASCAL VOC 2010 to create a new benchmarking dataset dubbed PPSS-12.Second, we propose an object-part based benchmarking procedure which quantifiesclassifiers' robustness to a range of visibility and contextual settings. Thebenchmarking procedure relies on a semantic similarity measure that naturallyaddresses potential semantic granularity differences between the categorylabels in training and test datasets, thus eliminating manual mapping. We useour procedure on the PPSS-12 dataset to benchmark top-performing classifierstrained on the ILSVRC-2012 dataset. Our results show that the proposedbenchmarking procedure enables additional differentiation amongstate-of-the-art object classifiers in terms of their ability to handle missingcontent and insufficient object detail. Given this capability for additionaldifferentiation, our approach can potentially supplement existing benchmarkingprocedures used in object recognition challenge leaderboards.

'Part'ly first among equals: Semantic part-based benchmarking for state-of-the-art object recognition systems