23 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
Temporal Deformable Convolutional Encoder-Decoder Networks for Video Captioning
It is well believed that video captioning is a fundamental but challenging
task in both computer vision and artificial intelligence fields. The prevalent
approach is to map an input video to a variable-length output sentence in a
sequence to sequence manner via Recurrent Neural Network (RNN). Nevertheless,
the training of RNN still suffers to some degree from vanishing/exploding
gradient problem, making the optimization difficult. Moreover, the inherently
recurrent dependency in RNN prevents parallelization within a sequence during
training and therefore limits the computations. In this paper, we present a
novel design --- Temporal Deformable Convolutional Encoder-Decoder Networks
(dubbed as TDConvED) that fully employ convolutions in both encoder and decoder
networks for video captioning. Technically, we exploit convolutional block
structures that compute intermediate states of a fixed number of inputs and
stack several blocks to capture long-term relationships. The structure in
encoder is further equipped with temporal deformable convolution to enable
free-form deformation of temporal sampling. Our model also capitalizes on
temporal attention mechanism for sentence generation. Extensive experiments are
conducted on both MSVD and MSR-VTT video captioning datasets, and superior
results are reported when comparing to conventional RNN-based encoder-decoder
techniques. More remarkably, TDConvED increases CIDEr-D performance from 58.8%
to 67.2% on MSVD.Comment: AAAI 201
Video Captioning via Hierarchical Reinforcement Learning
Video captioning is the task of automatically generating a textual
description of the actions in a video. Although previous work (e.g.
sequence-to-sequence model) has shown promising results in abstracting a coarse
description of a short video, it is still very challenging to caption a video
containing multiple fine-grained actions with a detailed description. This
paper aims to address the challenge by proposing a novel hierarchical
reinforcement learning framework for video captioning, where a high-level
Manager module learns to design sub-goals and a low-level Worker module
recognizes the primitive actions to fulfill the sub-goal. With this
compositional framework to reinforce video captioning at different levels, our
approach significantly outperforms all the baseline methods on a newly
introduced large-scale dataset for fine-grained video captioning. Furthermore,
our non-ensemble model has already achieved the state-of-the-art results on the
widely-used MSR-VTT dataset.Comment: CVPR 2018, with supplementary materia