183 research outputs found
Spatio-temporal Video Re-localization by Warp LSTM
The need for efficiently finding the video content a user wants is increasing
because of the erupting of user-generated videos on the Web. Existing
keyword-based or content-based video retrieval methods usually determine what
occurs in a video but not when and where. In this paper, we make an answer to
the question of when and where by formulating a new task, namely
spatio-temporal video re-localization. Specifically, given a query video and a
reference video, spatio-temporal video re-localization aims to localize
tubelets in the reference video such that the tubelets semantically correspond
to the query. To accurately localize the desired tubelets in the reference
video, we propose a novel warp LSTM network, which propagates the
spatio-temporal information for a long period and thereby captures the
corresponding long-term dependencies. Another issue for spatio-temporal video
re-localization is the lack of properly labeled video datasets. Therefore, we
reorganize the videos in the AVA dataset to form a new dataset for
spatio-temporal video re-localization research. Extensive experimental results
show that the proposed model achieves superior performances over the designed
baselines on the spatio-temporal video re-localization task
Few-Shot Transformation of Common Actions into Time and Space
This paper introduces the task of few-shot common action localization in time
and space. Given a few trimmed support videos containing the same but unknown
action, we strive for spatio-temporal localization of that action in a long
untrimmed query video. We do not require any class labels, interval bounds, or
bounding boxes. To address this challenging task, we introduce a novel few-shot
transformer architecture with a dedicated encoder-decoder structure optimized
for joint commonality learning and localization prediction, without the need
for proposals. Experiments on our reorganizations of the AVA and UCF101-24
datasets show the effectiveness of our approach for few-shot common action
localization, even when the support videos are noisy. Although we are not
specifically designed for common localization in time only, we also compare
favorably against the few-shot and one-shot state-of-the-art in this setting.
Lastly, we demonstrate that the few-shot transformer is easily extended to
common action localization per pixel
Predicting Deeper into the Future of Semantic Segmentation
The ability to predict and therefore to anticipate the future is an important
attribute of intelligence. It is also of utmost importance in real-time
systems, e.g. in robotics or autonomous driving, which depend on visual scene
understanding for decision making. While prediction of the raw RGB pixel values
in future video frames has been studied in previous work, here we introduce the
novel task of predicting semantic segmentations of future frames. Given a
sequence of video frames, our goal is to predict segmentation maps of not yet
observed video frames that lie up to a second or further in the future. We
develop an autoregressive convolutional neural network that learns to
iteratively generate multiple frames. Our results on the Cityscapes dataset
show that directly predicting future segmentations is substantially better than
predicting and then segmenting future RGB frames. Prediction results up to half
a second in the future are visually convincing and are much more accurate than
those of a baseline based on warping semantic segmentations using optical flow.Comment: Accepted to ICCV 2017. Supplementary material available on the
authors' webpage
Video Object Detection with an Aligned Spatial-Temporal Memory
We introduce Spatial-Temporal Memory Networks for video object detection. At
its core, a novel Spatial-Temporal Memory module (STMM) serves as the recurrent
computation unit to model long-term temporal appearance and motion dynamics.
The STMM's design enables full integration of pretrained backbone CNN weights,
which we find to be critical for accurate detection. Furthermore, in order to
tackle object motion in videos, we propose a novel MatchTrans module to align
the spatial-temporal memory from frame to frame. Our method produces
state-of-the-art results on the benchmark ImageNet VID dataset, and our
ablative studies clearly demonstrate the contribution of our different design
choices. We release our code and models at
http://fanyix.cs.ucdavis.edu/project/stmn/project.html
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