1,498 research outputs found
Exploiting Image-trained CNN Architectures for Unconstrained Video Classification
We conduct an in-depth exploration of different strategies for doing event
detection in videos using convolutional neural networks (CNNs) trained for
image classification. We study different ways of performing spatial and
temporal pooling, feature normalization, choice of CNN layers as well as choice
of classifiers. Making judicious choices along these dimensions led to a very
significant increase in performance over more naive approaches that have been
used till now. We evaluate our approach on the challenging TRECVID MED'14
dataset with two popular CNN architectures pretrained on ImageNet. On this
MED'14 dataset, our methods, based entirely on image-trained CNN features, can
outperform several state-of-the-art non-CNN models. Our proposed late fusion of
CNN- and motion-based features can further increase the mean average precision
(mAP) on MED'14 from 34.95% to 38.74%. The fusion approach achieves the
state-of-the-art classification performance on the challenging UCF-101 dataset
Beyond Short Snippets: Deep Networks for Video Classification
Convolutional neural networks (CNNs) have been extensively applied for image
recognition problems giving state-of-the-art results on recognition, detection,
segmentation and retrieval. In this work we propose and evaluate several deep
neural network architectures to combine image information across a video over
longer time periods than previously attempted. We propose two methods capable
of handling full length videos. The first method explores various convolutional
temporal feature pooling architectures, examining the various design choices
which need to be made when adapting a CNN for this task. The second proposed
method explicitly models the video as an ordered sequence of frames. For this
purpose we employ a recurrent neural network that uses Long Short-Term Memory
(LSTM) cells which are connected to the output of the underlying CNN. Our best
networks exhibit significant performance improvements over previously published
results on the Sports 1 million dataset (73.1% vs. 60.9%) and the UCF-101
datasets with (88.6% vs. 88.0%) and without additional optical flow information
(82.6% vs. 72.8%)
Temporal Localization of Fine-Grained Actions in Videos by Domain Transfer from Web Images
We address the problem of fine-grained action localization from temporally
untrimmed web videos. We assume that only weak video-level annotations are
available for training. The goal is to use these weak labels to identify
temporal segments corresponding to the actions, and learn models that
generalize to unconstrained web videos. We find that web images queried by
action names serve as well-localized highlights for many actions, but are
noisily labeled. To solve this problem, we propose a simple yet effective
method that takes weak video labels and noisy image labels as input, and
generates localized action frames as output. This is achieved by cross-domain
transfer between video frames and web images, using pre-trained deep
convolutional neural networks. We then use the localized action frames to train
action recognition models with long short-term memory networks. We collect a
fine-grained sports action data set FGA-240 of more than 130,000 YouTube
videos. It has 240 fine-grained actions under 85 sports activities. Convincing
results are shown on the FGA-240 data set, as well as the THUMOS 2014
localization data set with untrimmed training videos.Comment: Camera ready version for ACM Multimedia 201
Memory Based Online Learning of Deep Representations from Video Streams
We present a novel online unsupervised method for face identity learning from
video streams. The method exploits deep face descriptors together with a memory
based learning mechanism that takes advantage of the temporal coherence of
visual data. Specifically, we introduce a discriminative feature matching
solution based on Reverse Nearest Neighbour and a feature forgetting strategy
that detect redundant features and discard them appropriately while time
progresses. It is shown that the proposed learning procedure is asymptotically
stable and can be effectively used in relevant applications like multiple face
identification and tracking from unconstrained video streams. Experimental
results show that the proposed method achieves comparable results in the task
of multiple face tracking and better performance in face identification with
offline approaches exploiting future information. Code will be publicly
available.Comment: arXiv admin note: text overlap with arXiv:1708.0361
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