466 research outputs found
Style Transfer in Text: Exploration and Evaluation
Style transfer is an important problem in natural language processing (NLP).
However, the progress in language style transfer is lagged behind other
domains, such as computer vision, mainly because of the lack of parallel data
and principle evaluation metrics. In this paper, we propose to learn style
transfer with non-parallel data. We explore two models to achieve this goal,
and the key idea behind the proposed models is to learn separate content
representations and style representations using adversarial networks. We also
propose novel evaluation metrics which measure two aspects of style transfer:
transfer strength and content preservation. We access our models and the
evaluation metrics on two tasks: paper-news title transfer, and
positive-negative review transfer. Results show that the proposed content
preservation metric is highly correlate to human judgments, and the proposed
models are able to generate sentences with higher style transfer strength and
similar content preservation score comparing to auto-encoder.Comment: To appear in AAAI-1
Summarizing Videos with Attention
In this work we propose a novel method for supervised, keyshots based video
summarization by applying a conceptually simple and computationally efficient
soft, self-attention mechanism. Current state of the art methods leverage
bi-directional recurrent networks such as BiLSTM combined with attention. These
networks are complex to implement and computationally demanding compared to
fully connected networks. To that end we propose a simple, self-attention based
network for video summarization which performs the entire sequence to sequence
transformation in a single feed forward pass and single backward pass during
training. Our method sets a new state of the art results on two benchmarks
TvSum and SumMe, commonly used in this domain.Comment: Presented at ACCV2018 AIU2018 worksho
A Stepwise, Label-based Approach for Improving the Adversarial Training in Unsupervised Video Summarization
In this paper we present our work on improving the efficiency of adversarial training for unsupervised video summarization. Our starting point is the SUM-GAN model, which creates a representative summary based on the intuition that such a summary should make it possible to reconstruct a video that is indistinguishable from the original one. We build on a publicly available implementation of a variation of this model, that includes a linear compression layer to reduce the number of learned parameters and applies an incremental approach for training the different components of the architecture. After assessing the impact of these changes to the model’s performance, we propose a stepwise, label-based learning process to improve the training efficiency of the adversarial part of the model. Before evaluating our model’s efficiency, we perform a thorough study with respect to the used evaluation protocols and we examine the possible performance on two benchmarking datasets, namely SumMe and TVSum. Experimental evaluations and comparisons with the state of the art highlight the competitiveness of the proposed method. An ablation study indicates the benefit of each applied change on the model’s performance, and points out the advantageous role of the introduced stepwise, label-based training strategy on the learning efficiency of the adversarial part of the architecture
- …