15,898 research outputs found
Saliency-guided video classification via adaptively weighted learning
Video classification is productive in many practical applications, and the
recent deep learning has greatly improved its accuracy. However, existing works
often model video frames indiscriminately, but from the view of motion, video
frames can be decomposed into salient and non-salient areas naturally. Salient
and non-salient areas should be modeled with different networks, for the former
present both appearance and motion information, and the latter present static
background information. To address this problem, in this paper, video saliency
is predicted by optical flow without supervision firstly. Then two streams of
3D CNN are trained individually for raw frames and optical flow on salient
areas, and another 2D CNN is trained for raw frames on non-salient areas. For
the reason that these three streams play different roles for each class, the
weights of each stream are adaptively learned for each class. Experimental
results show that saliency-guided modeling and adaptively weighted learning can
reinforce each other, and we achieve the state-of-the-art results.Comment: 6 pages, 1 figure, accepted by ICME 201
CDC: Convolutional-De-Convolutional Networks for Precise Temporal Action Localization in Untrimmed Videos
Temporal action localization is an important yet challenging problem. Given a
long, untrimmed video consisting of multiple action instances and complex
background contents, we need not only to recognize their action categories, but
also to localize the start time and end time of each instance. Many
state-of-the-art systems use segment-level classifiers to select and rank
proposal segments of pre-determined boundaries. However, a desirable model
should move beyond segment-level and make dense predictions at a fine
granularity in time to determine precise temporal boundaries. To this end, we
design a novel Convolutional-De-Convolutional (CDC) network that places CDC
filters on top of 3D ConvNets, which have been shown to be effective for
abstracting action semantics but reduce the temporal length of the input data.
The proposed CDC filter performs the required temporal upsampling and spatial
downsampling operations simultaneously to predict actions at the frame-level
granularity. It is unique in jointly modeling action semantics in space-time
and fine-grained temporal dynamics. We train the CDC network in an end-to-end
manner efficiently. Our model not only achieves superior performance in
detecting actions in every frame, but also significantly boosts the precision
of localizing temporal boundaries. Finally, the CDC network demonstrates a very
high efficiency with the ability to process 500 frames per second on a single
GPU server. We will update the camera-ready version and publish the source
codes online soon.Comment: IEEE Conference on Computer Vision and Pattern Recognition (CVPR),
201
Tube Convolutional Neural Network (T-CNN) for Action Detection in Videos
Deep learning has been demonstrated to achieve excellent results for image
classification and object detection. However, the impact of deep learning on
video analysis (e.g. action detection and recognition) has been limited due to
complexity of video data and lack of annotations. Previous convolutional neural
networks (CNN) based video action detection approaches usually consist of two
major steps: frame-level action proposal detection and association of proposals
across frames. Also, these methods employ two-stream CNN framework to handle
spatial and temporal feature separately. In this paper, we propose an
end-to-end deep network called Tube Convolutional Neural Network (T-CNN) for
action detection in videos. The proposed architecture is a unified network that
is able to recognize and localize action based on 3D convolution features. A
video is first divided into equal length clips and for each clip a set of tube
proposals are generated next based on 3D Convolutional Network (ConvNet)
features. Finally, the tube proposals of different clips are linked together
employing network flow and spatio-temporal action detection is performed using
these linked video proposals. Extensive experiments on several video datasets
demonstrate the superior performance of T-CNN for classifying and localizing
actions in both trimmed and untrimmed videos compared to state-of-the-arts
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