5 research outputs found
Co-attention Propagation Network for Zero-Shot Video Object Segmentation
Zero-shot video object segmentation (ZS-VOS) aims to segment foreground
objects in a video sequence without prior knowledge of these objects. However,
existing ZS-VOS methods often struggle to distinguish between foreground and
background or to keep track of the foreground in complex scenarios. The common
practice of introducing motion information, such as optical flow, can lead to
overreliance on optical flow estimation. To address these challenges, we
propose an encoder-decoder-based hierarchical co-attention propagation network
(HCPN) capable of tracking and segmenting objects. Specifically, our model is
built upon multiple collaborative evolutions of the parallel co-attention
module (PCM) and the cross co-attention module (CCM). PCM captures common
foreground regions among adjacent appearance and motion features, while CCM
further exploits and fuses cross-modal motion features returned by PCM. Our
method is progressively trained to achieve hierarchical spatio-temporal feature
propagation across the entire video. Experimental results demonstrate that our
HCPN outperforms all previous methods on public benchmarks, showcasing its
effectiveness for ZS-VOS.Comment: accepted by IEEE Transactions on Image Processin
Hierarchical Feature Alignment Network for Unsupervised Video Object Segmentation
Optical flow is an easily conceived and precious cue for advancing
unsupervised video object segmentation (UVOS). Most of the previous methods
directly extract and fuse the motion and appearance features for segmenting
target objects in the UVOS setting. However, optical flow is intrinsically an
instantaneous velocity of all pixels among consecutive frames, thus making the
motion features not aligned well with the primary objects among the
corresponding frames. To solve the above challenge, we propose a concise,
practical, and efficient architecture for appearance and motion feature
alignment, dubbed hierarchical feature alignment network (HFAN). Specifically,
the key merits in HFAN are the sequential Feature AlignMent (FAM) module and
the Feature AdaptaTion (FAT) module, which are leveraged for processing the
appearance and motion features hierarchically. FAM is capable of aligning both
appearance and motion features with the primary object semantic
representations, respectively. Further, FAT is explicitly designed for the
adaptive fusion of appearance and motion features to achieve a desirable
trade-off between cross-modal features. Extensive experiments demonstrate the
effectiveness of the proposed HFAN, which reaches a new state-of-the-art
performance on DAVIS-16, achieving 88.7 Mean, i.e.,
a relative improvement of 3.5% over the best published result.Comment: Accepted by ECCV-202
Spectral Pre-Processing and Multivariate Calibration Methods for the Prediction of Wood Density in Chinese White Poplar by Visible and Near Infrared Spectroscopy
Wood density is a key indicator for tree functionality and end utilization. Appropriate chemometric methods play an important role in the successful prediction of wood density by visible and near infrared (Vis-NIR) spectroscopy. The objective of this study was to select appropriate pre-processing, variable selection and multivariate calibration techniques to improve the prediction accuracy of density in Chinese white poplar (Populus tomentosa carriere) wood. The Vis-NIR spectra were de-noised using four methods (lifting wavelet transform, LWT; wavelet transform, WT; multiplicative scatter correction, MSC; and standard normal variate, SNV), and four variable selection techniques, including successive projections algorithm (SPA), uninformative variables elimination (UVE), competitive adaptive reweighted sampling (CARS) and iteratively retains informative variables (IRIV), were compared to simplify the dimension of the high-dimensional spectral matrix. The non-linear models of generalized regression neural network (GRNN) and support vector machine (SVM) were performed using these selected variables. The results showed that the best prediction was obtained by GRNN models combined with the LWT and CARS method for Chinese white poplar wood density (Rp2 = 0.870; RMSEP = 13 Kg/m3; RPDp = 2.774)
Spectral Pre-Processing and Multivariate Calibration Methods for the Prediction of Wood Density in Chinese White Poplar by Visible and Near Infrared Spectroscopy
Wood density is a key indicator for tree functionality and end utilization. Appropriate chemometric methods play an important role in the successful prediction of wood density by visible and near infrared (Vis-NIR) spectroscopy. The objective of this study was to select appropriate pre-processing, variable selection and multivariate calibration techniques to improve the prediction accuracy of density in Chinese white poplar (Populus tomentosa carriere) wood. The Vis-NIR spectra were de-noised using four methods (lifting wavelet transform, LWT; wavelet transform, WT; multiplicative scatter correction, MSC; and standard normal variate, SNV), and four variable selection techniques, including successive projections algorithm (SPA), uninformative variables elimination (UVE), competitive adaptive reweighted sampling (CARS) and iteratively retains informative variables (IRIV), were compared to simplify the dimension of the high-dimensional spectral matrix. The non-linear models of generalized regression neural network (GRNN) and support vector machine (SVM) were performed using these selected variables. The results showed that the best prediction was obtained by GRNN models combined with the LWT and CARS method for Chinese white poplar wood density (Rp2 = 0.870; RMSEP = 13 Kg/m3; RPDp = 2.774)