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Found 6 papers, 2 papers with code
Global-and-Local Relative Position Embedding for Unsupervised Video Summarization
In order to summarize a content video properly, it is important to grasp the sequential structure of video as well as the long-term dependency between frames.
Single-Modal Entropy based Active Learning for Visual Question Answering
In this work, we address Active Learning in the multi-modal setting of Visual Question Answering (VQA).
MCDAL: Maximum Classifier Discrepancy for Active Learning
Recent state-of-the-art active learning methods have mostly leveraged Generative Adversarial Networks (GAN) for sample acquisition; however, GAN is usually known to suffer from instability and sensitivity to hyper-parameters.
Dealing with Missing Modalities in the Visual Question Answer-Difference Prediction Task through Knowledge Distillation
In this work, we address the issues of missing modalities that have arisen from the Visual Question Answer-Difference prediction task and find a novel method to solve the task at hand.
Hide-and-Tell: Learning to Bridge Photo Streams for Visual Storytelling
In this paper, we propose to explicitly learn to imagine a storyline that bridges the visual gap.
Ranked #7 on
Visual Storytelling
on VIST
Discriminative Feature Learning for Unsupervised Video Summarization
The proposed variance loss allows a network to predict output scores for each frame with high discrepancy which enables effective feature learning and significantly improves model performance.
Ranked #3 on
Unsupervised Video Summarization
on SumMe
Supervised Video Summarization
Unsupervised Video Summarization
