3 research outputs found
CDLT: A Dataset with Concept Drift and Long-Tailed Distribution for Fine-Grained Visual Categorization
Data is the foundation for the development of computer vision, and the
establishment of datasets plays an important role in advancing the techniques
of fine-grained visual categorization~(FGVC). In the existing FGVC datasets
used in computer vision, it is generally assumed that each collected instance
has fixed characteristics and the distribution of different categories is
relatively balanced. In contrast, the real world scenario reveals the fact that
the characteristics of instances tend to vary with time and exhibit a
long-tailed distribution. Hence, the collected datasets may mislead the
optimization of the fine-grained classifiers, resulting in unpleasant
performance in real applications. Starting from the real-world conditions and
to promote the practical progress of fine-grained visual categorization, we
present a Concept Drift and Long-Tailed Distribution dataset. Specifically, the
dataset is collected by gathering 11195 images of 250 instances in different
species for 47 consecutive months in their natural contexts. The collection
process involves dozens of crowd workers for photographing and domain experts
for labelling. Extensive baseline experiments using the state-of-the-art
fine-grained classification models demonstrate the issues of concept drift and
long-tailed distribution existed in the dataset, which require the attention of
future researches
An Incremental Change Detection Test Based on Density Difference Estimation
We propose incremental least squares density difference (LSDD) change detection method, an incremental test to detect changes in stationarity based on the difference between the unknown prechange and the post-change probability density functions (pdfs). The method is computationally light and, hence, adequate to process continuous datastreams, as those emerging from the Internet of Things and the big data framework. The incremental change detection test operates on two nonoverlapping data windows to estimate the LSDD between the two pdfs. We construct a theoretical framework that shows how the distribution of LSDD values follows a linear combination of Ï\u87 2 distributions and provides thresholds to control false positive rates. The proposed test can operate online, with needed estimates and thresholds computed incrementally as fresh samples come. Comprehensive experiments validate the effectiveness of the test both in detecting abrupt and drift types of changes