CLIP4Clip model transferred from the CLIP has been the de-factor standard to
solve the video clip retrieval task from frame-level input, triggering the
surge of CLIP4Clip-based models in the video-text retrieval domain. In this
work, we rethink the inherent limitation of widely-used mean pooling operation
in the frame features aggregation and investigate the adaptions of excitation
and aggregation design for discriminative video representation generation. We
present a novel excitationand-aggregation design, including (1) The excitation
module is available for capturing non-mutuallyexclusive relationships among
frame features and achieving frame-wise features recalibration, and (2) The
aggregation module is applied to learn exclusiveness used for frame
representations aggregation. Similarly, we employ the cascade of sequential
module and aggregation design to generate discriminative video representation
in the sequential type. Besides, we adopt the excitation design in the tight
type to obtain representative frame features for multi-modal interaction. The
proposed modules are evaluated on three benchmark datasets of MSR-VTT,
ActivityNet and DiDeMo, achieving MSR-VTT (43.9 R@1), ActivityNet (44.1 R@1)
and DiDeMo (31.0 R@1). They outperform the CLIP4Clip results by +1.2% (+0.5%),
+4.5% (+1.9%) and +9.5% (+2.7%) relative (absolute) improvements, demonstrating
the superiority of our proposed excitation and aggregation designs. We hope our
work will serve as an alternative for frame representations aggregation and
facilitate future research.

通过重新思考广泛使用的平均池化操作在帧特征聚合中固有的限制，并研究激励和聚合设计在生成具有区分性视频表示时的适应性，我们提出了一种新颖的激励和聚合设计。我们将这些模块评估在 MSR-VTT、ActivityNet 和 DiDeMo 的三个基准数据集上，实现了 MSR-VTT (43.9 R@1)、ActivityNet (44.1 R@1) 和 DiDeMo (31.0 R@1) 的结果，相对于 CLIP4Clip 的结果有 +1.2% (+0.5%)、+4.5% (+1.9%) 和 +9.5% (+2.7%) 的绝对和相对改进，证明了我们提出的激励和聚合设计的优越性。我们希望我们的工作可以作为帧特征聚合的替代方法，并促进未来的研究。