Video moment retrieval pursues an efficient and generalized solution to
identify the specific temporal segments within an untrimmed video that
correspond to a given language description. To achieve this goal, we provide a
generative diffusion-based framework called MomentDiff, which simulates a
typical human retrieval process from random browsing to gradual localization.
Specifically, we first diffuse the real span to random noise, and learn to
denoise the random noise to the original span with the guidance of similarity
between text and video. This allows the model to learn a mapping from arbitrary
random locations to real moments, enabling the ability to locate segments from
random initialization. Once trained, MomentDiff could sample random temporal
segments as initial guesses and iteratively refine them to generate an accurate
temporal boundary. Different from discriminative works (e.g., based on
learnable proposals or queries), MomentDiff with random initialized spans could
resist the temporal location biases from datasets. To evaluate the influence of
the temporal location biases, we propose two anti-bias datasets with location
distribution shifts, named Charades-STA-Len and Charades-STA-Mom. The
experimental results demonstrate that our efficient framework consistently
outperforms state-of-the-art methods on three public benchmarks, and exhibits
better generalization and robustness on the proposed anti-bias datasets. The
code, model, and anti-bias evaluation datasets are available at
this https URL

视频时刻检索通过一个名为 MomentDiff 的生成性扩散框架，实现了对未修剪视频中与给定语言描述相对应的特定时间段的高效广义解决方案。