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About this paper
Mengyu Zhao, Di Fu, Yongyu Xie, Jiaxing Zhang, Zhigang Yuan, Shirin Jalali, Yong Cao
InfoShot samples video frames for video-language models by segmenting videos into shots using pretrained visual features and selecting both a typical and an unusual frame from each shot. On SynFlash, at a matched budget of 0.5 frames per second, it achieves 84.70% average frame recall, versus 46.54% for VSUMM. This synthetic benchmark result does not establish gains on every task.
Key insights
- An information-theoretic objective, aiming to maximize retained information about both shot structure and within-shot deviations, provides an effective principle for robust and generalizable frame sampling.
- A two-stage approach of first segmenting videos into shots and then selecting complementary keyframes (one typical, one deviation-focused) within each shot is crucial for simultaneously capturing overall context and brief, critical events under tight frame budgets.
- Utilizing semantic features (e.g., from ResNet-50) for shot segmentation and deviation detection is essential for reliably identifying camouflaged or localized anomalies, outperforming simpler features like HSV histograms.
Summary by alphaXiv.
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