1,530 research outputs found
Hierarchical LSTM with Adjusted Temporal Attention for Video Captioning
Recent progress has been made in using attention based encoder-decoder
framework for video captioning. However, most existing decoders apply the
attention mechanism to every generated word including both visual words (e.g.,
"gun" and "shooting") and non-visual words (e.g. "the", "a"). However, these
non-visual words can be easily predicted using natural language model without
considering visual signals or attention. Imposing attention mechanism on
non-visual words could mislead and decrease the overall performance of video
captioning. To address this issue, we propose a hierarchical LSTM with adjusted
temporal attention (hLSTMat) approach for video captioning. Specifically, the
proposed framework utilizes the temporal attention for selecting specific
frames to predict the related words, while the adjusted temporal attention is
for deciding whether to depend on the visual information or the language
context information. Also, a hierarchical LSTMs is designed to simultaneously
consider both low-level visual information and high-level language context
information to support the video caption generation. To demonstrate the
effectiveness of our proposed framework, we test our method on two prevalent
datasets: MSVD and MSR-VTT, and experimental results show that our approach
outperforms the state-of-the-art methods on both two datasets
Video Storytelling: Textual Summaries for Events
Bridging vision and natural language is a longstanding goal in computer
vision and multimedia research. While earlier works focus on generating a
single-sentence description for visual content, recent works have studied
paragraph generation. In this work, we introduce the problem of video
storytelling, which aims at generating coherent and succinct stories for long
videos. Video storytelling introduces new challenges, mainly due to the
diversity of the story and the length and complexity of the video. We propose
novel methods to address the challenges. First, we propose a context-aware
framework for multimodal embedding learning, where we design a Residual
Bidirectional Recurrent Neural Network to leverage contextual information from
past and future. Second, we propose a Narrator model to discover the underlying
storyline. The Narrator is formulated as a reinforcement learning agent which
is trained by directly optimizing the textual metric of the generated story. We
evaluate our method on the Video Story dataset, a new dataset that we have
collected to enable the study. We compare our method with multiple
state-of-the-art baselines, and show that our method achieves better
performance, in terms of quantitative measures and user study.Comment: Published in IEEE Transactions on Multimedi
- …