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Action anticipation involves forecasting future actions by connecting pastevents to future ones. However, this reasoning ignores the real-life hierarchyof events which is considered to be composed of three main parts: past,present, and future. We argue that considering these three main parts and theirdependencies could improve performance. On the other hand, online actiondetection is the task of predicting actions in a streaming manner. In thiscase, one has access only to the past and present information. Therefore, inonline action detection (OAD) the existing approaches miss semantics or futureinformation which limits their performance. To sum up, for both of these tasks,the complete set of knowledge (past-present-future) is missing, which makes itchallenging to infer action dependencies, therefore having low performances. Toaddress this limitation, we propose to fuse both tasks into a single uniformarchitecture. By combining action anticipation and online action detection, ourapproach can cover the missing dependencies of future information in onlineaction detection. This method referred to as JOADAA, presents a uniform modelthat jointly performs action anticipation and online action detection. Wevalidate our proposed model on three challenging datasets: THUMOS'14, which isa sparsely annotated dataset with one action per time step, CHARADES, andMulti-THUMOS, two densely annotated datasets with more complex scenarios.JOADAA achieves SOTA results on these benchmarks for both tasks.

JOADAA: joint online action detection and action anticipation