Egocentric動画における効率的な組立活動予測のための適応的モダリティ選択を備えた目標誘導プロンプティング

With the functions of egocentric observation and multimodal perception equipped in augmented reality (AR) devices, the next generation of smart assistants has the potential to reduce human labor and enhance execution efficiency in assembly tasks. Among diverse assembly activity understanding tasks, anticipating the near future activities is crucial yet challenging, which can assist humans or agents to actively plan and engage in interactions with the environment. However, the existing egocentric activity anticipation methods still struggle to achieve a decent trade-off between accuracy and computational efficiency, hindering them to be deployed in practical applications. To address this dilemma, in this paper, we propose a goal-guided prompting framework with adaptive modality selection (GP-AMS), for assembly activity anticipation in egocentric videos. For bridging the semantic gap between the historical observations and unobserved future activities, we inject the inferred high-level goal clues into the constructed prompts, which are further utilized to guide a pre-trained vision-language (V-L) model to compensate relevant semantics of unseen future. Moreover, a mask-and-predict strategy is adopted with two imposed constraints, i.e., casual masking and probabilistic token-dropping, to mine the intrinsic associations between the assembly activities within a specific procedure. For maintaining the benefits of exploiting multimodal information while avoiding extensively increasing the computational burdens, an adaptive modality selection strategy is designed to train a policy network, which learns to dynamically decide which modalities should be sampled for processing by the anticipation model on a per observation time-step basis. By allocating major computation to the selected indicative modalities on-the-fly, the efficiency of the overall model can be improved, thus paving the way for feasibility on real-world devices. Extensive experimental results on two public data sets validate that the proposed method yields not only consistent improvements in anticipation accuracy, but also significant savings in computation budgets.