3 research outputs found
Global and Local Sensitivity Guided Key Salient Object Re-augmentation for Video Saliency Detection
The existing still-static deep learning based saliency researches do not
consider the weighting and highlighting of extracted features from different
layers, all features contribute equally to the final saliency decision-making.
Such methods always evenly detect all "potentially significant regions" and
unable to highlight the key salient object, resulting in detection failure of
dynamic scenes. In this paper, based on the fact that salient areas in videos
are relatively small and concentrated, we propose a \textbf{key salient object
re-augmentation method (KSORA) using top-down semantic knowledge and bottom-up
feature guidance} to improve detection accuracy in video scenes. KSORA includes
two sub-modules (WFE and KOS): WFE processes local salient feature selection
using bottom-up strategy, while KOS ranks each object in global fashion by
top-down statistical knowledge, and chooses the most critical object area for
local enhancement. The proposed KSORA can not only strengthen the saliency
value of the local key salient object but also ensure global saliency
consistency. Results on three benchmark datasets suggest that our model has the
capability of improving the detection accuracy on complex scenes. The
significant performance of KSORA, with a speed of 17FPS on modern GPUs, has
been verified by comparisons with other ten state-of-the-art algorithms.Comment: 6 figures, 10 page
Cross-Modal Weighting Network for RGB-D Salient Object Detection
Depth maps contain geometric clues for assisting Salient Object Detection
(SOD). In this paper, we propose a novel Cross-Modal Weighting (CMW) strategy
to encourage comprehensive interactions between RGB and depth channels for
RGB-D SOD. Specifically, three RGB-depth interaction modules, named CMW-L,
CMW-M and CMW-H, are developed to deal with respectively low-, middle- and
high-level cross-modal information fusion. These modules use Depth-to-RGB
Weighing (DW) and RGB-to-RGB Weighting (RW) to allow rich cross-modal and
cross-scale interactions among feature layers generated by different network
blocks. To effectively train the proposed Cross-Modal Weighting Network
(CMWNet), we design a composite loss function that summarizes the errors
between intermediate predictions and ground truth over different scales. With
all these novel components working together, CMWNet effectively fuses
information from RGB and depth channels, and meanwhile explores object
localization and details across scales. Thorough evaluations demonstrate CMWNet
consistently outperforms 15 state-of-the-art RGB-D SOD methods on seven popular
benchmarks.Comment: Accepted in ECCV2020. Code: https://github.com/MathLee/CMWNe
Improving the Accuracy of Global Forecasting Models using Time Series Data Augmentation
Forecasting models that are trained across sets of many time series, known as
Global Forecasting Models (GFM), have shown recently promising results in
forecasting competitions and real-world applications, outperforming many
state-of-the-art univariate forecasting techniques. In most cases, GFMs are
implemented using deep neural networks, and in particular Recurrent Neural
Networks (RNN), which require a sufficient amount of time series to estimate
their numerous model parameters. However, many time series databases have only
a limited number of time series. In this study, we propose a novel, data
augmentation based forecasting framework that is capable of improving the
baseline accuracy of the GFM models in less data-abundant settings. We use
three time series augmentation techniques: GRATIS, moving block bootstrap
(MBB), and dynamic time warping barycentric averaging (DBA) to synthetically
generate a collection of time series. The knowledge acquired from these
augmented time series is then transferred to the original dataset using two
different approaches: the pooled approach and the transfer learning approach.
When building GFMs, in the pooled approach, we train a model on the augmented
time series alongside the original time series dataset, whereas in the transfer
learning approach, we adapt a pre-trained model to the new dataset. In our
evaluation on competition and real-world time series datasets, our proposed
variants can significantly improve the baseline accuracy of GFM models and
outperform state-of-the-art univariate forecasting methods