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Found 7 papers, 3 papers with code
Efficient Convolution and Transformer-Based Network for Video Frame Interpolation
This network reduces the memory burden by close to 50% and runs up to four times faster during inference time compared to existing transformer-based interpolation methods.
Query-based Video Summarization with Pseudo Label Supervision
Existing datasets for manually labelled query-based video summarization are costly and thus small, limiting the performance of supervised deep video summarization models.
Complexity Reduction of Learned In-Loop Filtering in Video Coding
In video coding, in-loop filters are applied on reconstructed video frames to enhance their perceptual quality, before storing the frames for output.
Improved CNN-based Learning of Interpolation Filters for Low-Complexity Inter Prediction in Video Coding
The approach requires a single neural network to be trained from which a full quarter-pixel interpolation filter set is derived, as the network is easily interpretable due to its linear structure.
Towards Transparent Application of Machine Learning in Video Processing
Machine learning techniques for more efficient video compression and video enhancement have been developed thanks to breakthroughs in deep learning.
GPT2MVS: Generative Pre-trained Transformer-2 for Multi-modal Video Summarization
Traditional video summarization methods generate fixed video representations regardless of user interest.
Interpreting CNN for Low Complexity Learned Sub-pixel Motion Compensation in Video Coding
Deep learning has shown great potential in image and video compression tasks.
