2,710 research outputs found
Video Saliency Detection by 3D Convolutional Neural Networks
Different from salient object detection methods for still images, a key
challenging for video saliency detection is how to extract and combine spatial
and temporal features. In this paper, we present a novel and effective approach
for salient object detection for video sequences based on 3D convolutional
neural networks. First, we design a 3D convolutional network (Conv3DNet) with
the input as three video frame to learn the spatiotemporal features for video
sequences. Then, we design a 3D deconvolutional network (Deconv3DNet) to
combine the spatiotemporal features to predict the final saliency map for video
sequences. Experimental results show that the proposed saliency detection model
performs better in video saliency prediction compared with the state-of-the-art
video saliency detection methods
Video Salient Object Detection via Fully Convolutional Networks
This paper proposes a deep learning model to efficiently detect salient regions in videos. It addresses two important issues: 1) deep video saliency model training with the absence of sufficiently large and pixel-wise annotated video data and 2) fast video saliency training and detection. The proposed deep video saliency network consists of two modules, for capturing the spatial and temporal saliency information, respectively. The dynamic saliency model, explicitly incorporating saliency estimates from the static saliency model, directly produces spatiotemporal saliency inference without time-consuming optical flow computation. We further propose a novel data augmentation technique that simulates video training data from existing annotated image data sets, which enables our network to learn diverse saliency information and prevents overfitting with the limited number of training videos. Leveraging our synthetic video data (150K video sequences) and real videos, our deep video saliency model successfully learns both spatial and temporal saliency cues, thus producing accurate spatiotemporal saliency estimate. We advance the state-of-the-art on the densely annotated video segmentation data set (MAE of .06) and the Freiburg-Berkeley Motion Segmentation data set (MAE of .07), and do so with much improved speed (2 fps with all steps)
A deep-learning based feature hybrid framework for spatiotemporal saliency detection inside videos
Although research on detection of saliency and visual attention has been active over recent years, most of the existing work focuses on still image rather than video based saliency. In this paper, a deep learning based hybrid spatiotemporal saliency feature extraction framework is proposed for saliency detection from video footages. The deep learning model is used for the extraction of high-level features from raw video data, and they are then integrated with other high-level features. The deep learning network has been found extremely effective for extracting hidden features than that of conventional handcrafted methodology. The effectiveness for using hybrid high-level features for saliency detection in video is demonstrated in this work. Rather than using only one static image, the proposed deep learning model take several consecutive frames as input and both the spatial and temporal characteristics are considered when computing saliency maps. The efficacy of the proposed hybrid feature framework is evaluated by five databases with human gaze complex scenes. Experimental results show that the proposed model outperforms five other state-of-the-art video saliency detection approaches. In addition, the proposed framework is found useful for other video content based applications such as video highlights. As a result, a large movie clip dataset together with labeled video highlights is generated
Excitation Backprop for RNNs
Deep models are state-of-the-art for many vision tasks including video action
recognition and video captioning. Models are trained to caption or classify
activity in videos, but little is known about the evidence used to make such
decisions. Grounding decisions made by deep networks has been studied in
spatial visual content, giving more insight into model predictions for images.
However, such studies are relatively lacking for models of spatiotemporal
visual content - videos. In this work, we devise a formulation that
simultaneously grounds evidence in space and time, in a single pass, using
top-down saliency. We visualize the spatiotemporal cues that contribute to a
deep model's classification/captioning output using the model's internal
representation. Based on these spatiotemporal cues, we are able to localize
segments within a video that correspond with a specific action, or phrase from
a caption, without explicitly optimizing/training for these tasks.Comment: CVPR 2018 Camera Ready Versio
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