A Simple Recipe for Contrastively Pre-training Video-First Encoders Beyond 16 Frames
This work improves video understanding for applications requiring long-range temporal dependencies, though it is incremental as it builds on existing contrastive pre-training methods without new architectural complexity.
The paper tackled the problem of scaling video-first encoders to process long videos by addressing memory bottlenecks and poor video-language alignment, achieving state-of-the-art performance on benchmarks like YouCook2 and EgoSchema with up to 4.3 minutes of video at 1 FPS using a 1B-parameter model.
Understanding long, real-world videos requires modeling of long-range visual dependencies. To this end, we explore video-first architectures, building on the common paradigm of transferring large-scale, image--text models to video via shallow temporal fusion. However, we expose two limitations to the approach: (1) decreased spatial capabilities, likely due to poor video--language alignment in standard video datasets, and (2) higher memory consumption, bottlenecking the number of frames that can be processed. To mitigate the memory bottleneck, we systematically analyze the memory/accuracy trade-off of various efficient methods: factorized attention, parameter-efficient image-to-video adaptation, input masking, and multi-resolution patchification. Surprisingly, simply masking large portions of the video (up to 75%) during contrastive pre-training proves to be one of the most robust ways to scale encoders to videos up to 4.3 minutes at 1 FPS. Our simple approach for training long video-to-text models, which scales to 1B parameters, does not add new architectural complexity and is able to outperform the popular paradigm of using much larger LLMs as an information aggregator over segment-based information on benchmarks with long-range temporal dependencies (YouCook2, EgoSchema).