On the Importance of Signer Overlap for Sign Language Detection
This work addresses a methodological issue for researchers in sign language detection, aiming to improve accuracy and generalization, but it is incremental as it focuses on data partitioning rather than new detection methods.
The paper tackles the problem of signer overlap between train and test partitions in sign language detection benchmarks, which leads to overly positive results that do not generalize well, and quantifies this with relative accuracy decreases of 4.17% on the DGS corpus and 6.27% on Signing in the Wild.
Sign language detection, identifying if someone is signing or not, is becoming crucially important for its applications in remote conferencing software and for selecting useful sign data for training sign language recognition or translation tasks. We argue that the current benchmark data sets for sign language detection estimate overly positive results that do not generalize well due to signer overlap between train and test partitions. We quantify this with a detailed analysis of the effect of signer overlap on current sign detection benchmark data sets. Comparing accuracy with and without overlap on the DGS corpus and Signing in the Wild, we observed a relative decrease in accuracy of 4.17% and 6.27%, respectively. Furthermore, we propose new data set partitions that are free of overlap and allow for more realistic performance assessment. We hope this work will contribute to improving the accuracy and generalization of sign language detection systems.