KAUST DepartmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Computer Science Program
Electrical Engineering Program
Visual Computing Center (VCC)
KAUST Grant NumberOSR-CRG2017-3405
Online Publication Date2018-10-07
Print Publication Date2018
Permanent link to this recordhttp://hdl.handle.net/10754/630247
MetadataShow full item record
AbstractDespite the recent progress in video understanding and the continuous rate of improvement in temporal action localization throughout the years, it is still unclear how far (or close?) we are to solving the problem. To this end, we introduce a new diagnostic tool to analyze the performance of temporal action detectors in videos and compare different methods beyond a single scalar metric. We exemplify the use of our tool by analyzing the performance of the top rewarded entries in the latest ActivityNet action localization challenge. Our analysis shows that the most impactful areas to work on are: strategies to better handle temporal context around the instances, improving the robustness w.r.t. the instance absolute and relative size, and strategies to reduce the localization errors. Moreover, our experimental analysis finds the lack of agreement among annotator is not a major roadblock to attain progress in the field. Our diagnostic tool is publicly available to keep fueling the minds of other researchers with additional insights about their algorithms.
CitationAlwassel H, Caba Heilbron F, Escorcia V, Ghanem B (2018) Diagnosing Error in Temporal Action Detectors. Lecture Notes in Computer Science: 264–280. Available: http://dx.doi.org/10.1007/978-3-030-01219-9_16.
SponsorsThis publication is based upon work supported by the King Abdullah University of Science and Technology (KAUST) Office of Sponsored Research (OSR) under Award No. OSR-CRG2017-3405.
Conference/Event name15th European Conference on Computer Vision, ECCV 2018