Search Results for author:
Found 6 papers, 3 papers with code
Learning Trailer Moments in Full-Length Movies with Co-Contrastive Attention
We introduce a novel ranking network that utilizes the Co-Attention between movies and trailers as guidance to generate the training pairs, where the moments highly corrected with trailers are expected to be scored higher than the uncorrelated moments.
Learning Trailer Moments in Full-Length Movies
A movie's key moments stand out of the screenplay to grab an audience's attention and make movie browsing efficient.
Unsupervised Domain Adaptation via Calibrating Uncertainties
Unsupervised domain adaptation (UDA) aims at inferring class labels for unlabeled target domain given a related labeled source dataset.
Sharpen Focus: Learning with Attention Separability and Consistency
Recent developments in gradient-based attention modeling have seen attention maps emerge as a powerful tool for interpreting convolutional neural networks.
Show Me a Story: Towards Coherent Neural Story Illustration
We propose an end-to-end network for the visual illustration of a sequence of sentences forming a story.
Dual Iterative Hard Thresholding: From Non-convex Sparse Minimization to Non-smooth Concave Maximization
It remains open to explore duality theory and algorithms in such a non-convex and NP-hard problem setting.
