1,004 research outputs found
Move Forward and Tell: A Progressive Generator of Video Descriptions
We present an efficient framework that can generate a coherent paragraph to
describe a given video. Previous works on video captioning usually focus on
video clips. They typically treat an entire video as a whole and generate the
caption conditioned on a single embedding. On the contrary, we consider videos
with rich temporal structures and aim to generate paragraph descriptions that
can preserve the story flow while being coherent and concise. Towards this
goal, we propose a new approach, which produces a descriptive paragraph by
assembling temporally localized descriptions. Given a video, it selects a
sequence of distinctive clips and generates sentences thereon in a coherent
manner. Particularly, the selection of clips and the production of sentences
are done jointly and progressively driven by a recurrent network -- what to
describe next depends on what have been said before. Here, the recurrent
network is learned via self-critical sequence training with both sentence-level
and paragraph-level rewards. On the ActivityNet Captions dataset, our method
demonstrated the capability of generating high-quality paragraph descriptions
for videos. Compared to those by other methods, the descriptions produced by
our method are often more relevant, more coherent, and more concise.Comment: Accepted by ECCV 201
Areas of Attention for Image Captioning
We propose "Areas of Attention", a novel attention-based model for automatic
image captioning. Our approach models the dependencies between image regions,
caption words, and the state of an RNN language model, using three pairwise
interactions. In contrast to previous attention-based approaches that associate
image regions only to the RNN state, our method allows a direct association
between caption words and image regions. During training these associations are
inferred from image-level captions, akin to weakly-supervised object detector
training. These associations help to improve captioning by localizing the
corresponding regions during testing. We also propose and compare different
ways of generating attention areas: CNN activation grids, object proposals, and
spatial transformers nets applied in a convolutional fashion. Spatial
transformers give the best results. They allow for image specific attention
areas, and can be trained jointly with the rest of the network. Our attention
mechanism and spatial transformer attention areas together yield
state-of-the-art results on the MSCOCO dataset.o meaningful latent semantic
structure in the generated captions.Comment: Accepted in ICCV 201
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