Google researchers address the challenges of achieving a comprehensive understanding of diverse video content by introducing a novel encoder model, VideoPrism. Existing models in video understanding have struggled with various tasks with complex systems and motion-focused reasoning and have demonstrated poor performance on different benchmarks. The researchers set out to develop a general-purpose video encoder that can effectively address a wide range of video understanding tasks with minimal adaptation.
Existing video understanding models have made significant progress, but they are not there yet. Some models leverage text associated with videos for learning, and others focus solely on video cues, limiting effective capture of both appearance and motion cues. VideoPrism proposes an approach that integrates video and text modalities during pre-training. It introduces a two-stage pre-training framework that combines contrastive learning with masked video modeling. This method allows the model to learn semantic representations from both video-text pairs and video-only data.
The VideoPrism architecture is based on the Vision Transformer (ViT) with modifications for space-time factorization. During pre-training, the model first aligns video and text embeddings through contrastive learning and then continues training with video-only data using masked video modeling. This two-stage approach is complemented by global-local distillation and token mixing techniques to improve model performance. Extensive evaluations of various video understanding tasks demonstrate that VideoPrism achieves state-of-the-art performance on 30 of 33 benchmarks, demonstrating its strong generalization and effectiveness in capturing appearance and motion cues.
Google researchers address the challenge of building a fundamental video model with their next-generation VideoPrism model for comprehensive video understanding. The proposed method combines contrastive learning with masked video modeling in a two-stage pre-training framework, resulting in a model that excels in a wide range of video understanding tasks.
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Pragati Jhunjhunwala is a Consulting Intern at MarktechPost. She is currently pursuing B.tech from the Indian Institute of technology (IIT), Kharagpur. She is a technology enthusiast and has a keen interest in the scope of data science software and applications. She is always reading about the advancements in different fields of ai and ML.
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