# IOD-CNN: Integrating Object Detection Networks for Event Recognition Source: [IOD-CNN: Integrating Object Detection Networks for Event Recognition]() Collection index: [llms.txt]() Data updated: 2025-10-27T05:52:51.467Z Authors: Sungmin Eum, Hyungtae Lee, Heesung Kwon, David Doermann Published: 2017-03-21T21:05:21.000Z ## Abstract Many previous methods have showed the importance of considering semantically relevant objects for performing event recognition, yet none of the methods have exploited the power of deep convolutional neural networks to directly integrate relevant object information into a unified network. We present a novel unified deep CNN architecture which integrates architecturally different, yet semantically-related object detection networks to enhance the performance of the event recognition task. Our architecture allows the sharing of the convolutional layers and a fully connected layer which effectively integrates event recognition, rigid object detection and non-rigid object detection. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()