动量引导的语义预测（MoFore）用于自监督视频表示学习
Momentum-Guided Semantic Forecasting (MoFore) for Self-Supervised Video Representation Learning
AI总结：
提出MoFore框架，通过预测未来潜在嵌入进行自监督视频表示学习，结合对比正则化防止表示崩溃，在UCF101上验证了时间一致性和语义结构。
摘要：
自监督视频表示学习最近通过对比学习、掩码重建和预测表示学习取得了进展。基于重建的方法如MAE和VideoMAE通过恢复掩码视觉内容来学习表示，而对比方法如CLIP通过表示对齐学习语义有意义的嵌入空间。在这项工作中，我们提出了一种动量引导的语义预测框架（MoFore）用于自监督视频表示学习。该方法不是优化像素级重建或任务特定的语义对齐，而是通过从时间上遥远的上下文片段预测未来的潜在嵌入来学习时间预测性视频表示。为了提高跨时间尺度的鲁棒性，我们进一步引入了训练期间的随机时间间隔预测。该框架将预测性潜在预测与对比正则化相结合，以鼓励时间一致性同时防止表示崩溃。在UCF101数据集上的实验表明，所提出的框架在训练期间不使用动作标签的情况下学习了时间一致且语义有意义的视频表示。定量分析显示学习到的嵌入空间具有强时间稳定性和涌现的类别级结构，而定性检索实验揭示了跨相关活动的运动感知组织。总体而言，结果表明长程潜在预测为自监督视频表示学习提供了一种有效且计算高效的方法，而不依赖于基于重建的目标。
英文摘要：
Self-supervised video representation learning has recently advanced through contrastive learning, masked reconstruction, and predictive representation learning. Reconstruction-based approaches such as MAE and VideoMAE learn representations by recovering masked visual content \cite{he2022mae,tong2022videomae}, while contrastive methods such as CLIP learn semantically meaningful embedding spaces through representation alignment \cite{radford2021clip}. In this work, we introduce a Momentum-Guided Semantic Forecasting framework (MoFore) for self-supervised video representation learning. Instead of optimizing for pixel-level reconstruction or task-specific semantic alignment, the proposed method learns temporally predictive video representations by forecasting future latent embeddings from temporally distant context clips. To improve robustness across temporal scales, we further introduce randomized temporal-gap forecasting during training. The framework combines predictive latent forecasting with contrastive regularization to encourage temporal consistency while preventing representation collapse. Experiments on the UCF101 dataset demonstrate that the proposed framework learns temporally consistent and semantically meaningful video representations without using action labels during training. Quantitative analysis shows strong temporal stability and emergent category-level structure in the learned embedding space, while qualitative retrieval experiments reveal motion-aware organization across related activities. Overall, the results suggest that long-range latent forecasting provides an effective and computationally efficient approach for self-supervised video representation learning without relying on reconstruction-based objectives.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
