Search Results for author:
Found 7 papers, 1 papers with code
Collaborative Method for Incremental Learning on Classification and Generation
In this paper, we introduce a novel algorithm, Incremental Class Learning with Attribute Sharing (ICLAS), for incremental class learning with deep neural networks.
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
Gaining Extra Supervision via Multi-task learning for Multi-Modal Video Question Answering
This paper proposes a method to gain extra supervision via multi-task learning for multi-modal video question answering.
Arbitrary Shape Scene Text Detection with Adaptive Text Region Representation
Here, recurrent neural network based adaptive text region representation is proposed for text region refinement, where a pair of boundary points are predicted each time step until no new points are found.
Progressive Attention Memory Network for Movie Story Question Answering
To overcome these challenges, PAMN involves three main features: (1) progressive attention mechanism that utilizes cues from both question and answer to progressively prune out irrelevant temporal parts in memory, (2) dynamic modality fusion that adaptively determines the contribution of each modality for answering the current question, and (3) belief correction answering scheme that successively corrects the prediction score on each candidate answer.
Ranked #2 on
Video Story QA
on MovieQA
Learning Not to Learn: Training Deep Neural Networks with Biased Data
We propose a novel regularization algorithm to train deep neural networks, in which data at training time is severely biased.
Pivot Correlational Neural Network for Multimodal Video Categorization
The architecture is trained to maximizes the correlation between the hidden states as well as the predictions of the modal-agnostic pivot stream and modal-specific stream in the network.
