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A Hybrid Egocentric Activity Anticipation Framework via Memory-Augmented Recurrent and One-shot Representation Forecasting

Egocentric activity anticipation involves identifying the interacted objects and target action patterns in the near future. A standard activity anticipation paradigm is re-currently forecasting future representations to compensate the missing activity semantics of the unobserved sequence. However, the limitations of current recursive prediction models arise from two aspects: (i) The vanilla recurrent units are prone to accumulated errors in relatively long periods of anticipation. (ii) The anticipated representations may be insufficient to reflect the desired semantics of the target activity, due to lack of contextual clues. To address these issues, we propose “HRO ”, a hybrid framework that integrates both the memory-augmented recurrent and one-shot representation forecasting strategies. Specifically, to solve the limitation (i), we introduce a memory-augmented contrastive learning paradigm to regulate the process of the recurrent representation forecasting. Since the external memory bank maintains long-term prototypical activity semantics, it can guarantee that the anticipated representations are reconstructed from the discriminative activity prototypes. To further guide the learning of the memory bank, two auxiliary loss functions are designed, based on the diversity and sparsity mechanisms, respectively. Furthermore, to resolve the limitation (ii), a one-shot transferring paradigm is proposed to enrich the forecasted representations, by distilling the holistic activity semantics after the target anticipation moment, in the offline training. Extensive experimental results on two large-scale data sets validate the effectiveness of our proposed HRO method.