52,424 research outputs found
AV-MaskEnhancer: Enhancing Video Representations through Audio-Visual Masked Autoencoder
Learning high-quality video representation has shown significant applications
in computer vision and remains challenging. Previous work based on mask
autoencoders such as ImageMAE and VideoMAE has proven the effectiveness of
learning representations in images and videos through reconstruction strategy
in the visual modality. However, these models exhibit inherent limitations,
particularly in scenarios where extracting features solely from the visual
modality proves challenging, such as when dealing with low-resolution and
blurry original videos. Based on this, we propose AV-MaskEnhancer for learning
high-quality video representation by combining visual and audio information.
Our approach addresses the challenge by demonstrating the complementary nature
of audio and video features in cross-modality content. Moreover, our result of
the video classification task on the UCF101 dataset outperforms the existing
work and reaches the state-of-the-art, with a top-1 accuracy of 98.8% and a
top-5 accuracy of 99.9%
Deep Video Color Propagation
Traditional approaches for color propagation in videos rely on some form of
matching between consecutive video frames. Using appearance descriptors, colors
are then propagated both spatially and temporally. These methods, however, are
computationally expensive and do not take advantage of semantic information of
the scene. In this work we propose a deep learning framework for color
propagation that combines a local strategy, to propagate colors frame-by-frame
ensuring temporal stability, and a global strategy, using semantics for color
propagation within a longer range. Our evaluation shows the superiority of our
strategy over existing video and image color propagation methods as well as
neural photo-realistic style transfer approaches.Comment: BMVC 201
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