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Furthermore, many of the aforementioned approaches rely on the split-and-aggregate approach when processing long videos, where inputs are divided into sections which are processed separately by the model. Though many of the downstream tasks considered by these models do not specifically require temporal reasoning over the input videos, their applicability to tasks requiring temporal reasoning is limited, as it keeps models from capturing interactions across segments. In light of this, we present Locformer, a Transformer-based model for the task of temporal moment localization which operates at a constant memory footprint regardless of the input length, as shown in Figure 1. The success of the Locformer relies on two key ideas. Firstly, Locformer incorporates Stochastic Bucket-wise Feature Sampling (SBFS), which splits the sequence of input video feature into a fixed number of buckets and selects a single feature per bucket per iteration using a stochastic approach during training. While bucketing enables us to keep the memory budget limited by effectively shortening the input sequence length to the number of buckets, the stochastic nature of our approach allows us to obtain a better coverage of the video with sufficient stochasticity, obtaining a feature sample-set that is representative of the video content for the task at hand.




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