The article introduces a novel approach to long-context video understanding using a memory-consolidated vision transformer (MC-ViT). Standard transformer-based video encoders struggle with long-contexts due to their quadratic complexity. The researchers…
Continue readingThe article introduces ‘FunnyNet-W’, a model that relies on cross- and self-attention for visual, audio, and text data to predict funny moments in videos. Unlike most methods that rely on…
Continue readingThis paper presents a method to improve the understanding and analysis of long-format videos. The authors propose a query-aware method for localizing and understanding relations in long videos by using…
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