Motion-Attentive Transition for Zero-Shot Video Object Segmentation

Zhou, Tianfei; Wang, Shunzhou; Zhou, Yi; Yao, Yazhou; Li, Jianwu; Shao, Ling

doi:10.1609/aaai.v34i07.7008

Cited by 173 publications

(141 citation statements)

References 38 publications

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“…The contrast between different features is enhanced and useful features are more prominent. Many attention mechanisms such as MAT [22], IHSM&EFRM [23], CBAM [24], SE [25], and ECA [26] have been applied in various visual tasks. MAT module consists of a soft attention unit and an attention transition unit, which allows the transition of attentive motion features to enhance appearance learning at each convolution stage and enrich spatio-temporal object features.…”

Section: Eca Attention Mechanismmentioning

confidence: 99%

Classification of Ground-Based Cloud Images by Improved Combined Convolutional Network

et al. 2022

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Changes in clouds can affect the outpower of photovoltaics (PVs). Ground-based cloud images classification is an important prerequisite for PV power prediction. Due to the intra-class difference and inter-class similarity of cloud images, the classical convolutional network is obviously insufficient in distinguishing ability. In this paper, a classification method of ground-based cloud images by improved combined convolutional network is proposed. To solve the problem of sub-network overfitting caused by redundancy of pixel information, overlap pooling kernel is used to enhance the elimination effect of information redundancy in the pooling layer. A new channel attention module, ECA-WS (Efficient Channel Attention–Weight Sharing), is introduced to improve the network’s ability to express channel information. The decision fusion algorithm is employed to fuse the outputs of sub-networks with multi-scales. According to the number of cloud images in each category, different weights are applied to the fusion results, which solves the problem of network scale limitation and dataset imbalance. Experiments are carried out on the open MGCD dataset and the self-built NRELCD dataset. The results show that the proposed model has significantly improved the classification accuracy compared with the classical network and the latest algorithms.

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Section: Eca Attention Mechanismmentioning

confidence: 99%

Classification of Ground-Based Cloud Images by Improved Combined Convolutional Network

et al. 2022

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“…Wang et al [36] focused on the relations between pixels across different images. Zhou et al [37] used the optical flow and attention mechanism to segment the video objects; image co-segmentation requires a set of images containing objects from the same category as a weak form of supervision. Rother et al [38] minimised an energy function to segment image pairs.…”

Section: Image Segmentation From Unlabeled Datamentioning

confidence: 99%

Image Segmentation from Sparse Decomposition with a Pretrained Object-Detection Network

Ding

et al. 2022

Electronics

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Annotations for image segmentation are expensive and time-consuming. In contrast to image segmentation, the task of object detection is in general easier in terms of the acquisition of labeled training data and the design of training models. In this paper, we combine the idea of unsupervised learning and a pretrained object-detection network to perform image segmentation, without using expensive segmentation labels. Specially, we designed a pretext task based on the sparse decomposition of object instances in videos to obtain the segmentation mask of the objects, which benefits from the sparsity of image instances and the inter-frame structure of videos. To improve the accuracy of identifying the ’right’ object, we used a pretrained object-detection network to provide the location information of the object instances, and propose an Object Location Segmentation (OLSeg) model of three branches with bounding box prior. The model is trained from videos and is able to capture the foreground, background and segmentation mask in a single image. The performance gain benefits from the sparsity of object instances (the foreground and background in our experiments) and the provided location information (bounding box prior), which work together to produce a comprehensive and robust visual representation for the input. The experimental results demonstrate that the proposed model boosts the performance effectively on various image segmentation benchmarks.

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“…The crowd counting tasks have attracted the interest of many researchers in the field of computer vision because crowd counting has a high value in a number of important practical issues such as group safety warning [8] [46] and the perception of the traffic domain [17] and so on.…”

Section: A Crowd Countingmentioning

confidence: 99%

AAFM: Adaptive Attention Fusion Mechanism for Crowd Counting

2020

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CNN-based crowd counting methods have achieved great progress in recent years. However, most of these CNN-based crowd counting methods do not make full use of contextual information, which contains high-level semantic features and low-level detail features from different receptive fields of CNN. But rich contextual information is important to solve the scale variation problem of crowd counting. So the precision of previous CNN-based crowd counting methods is decreased. To solve this problem, we propose an adaptive attention fusion mechanism (AAFM). AAFM can use multi-scale features from different receptive fields of CNN effectively. It integrates the convolution network for feature learning and the attention mechanism for multi-scale features fusion. We apply the first 13 convolution layers of VGG-16 as the encoder module to extract the base features. Then, the base features are fed into the decoder module. The decoder module mainly contains Density Regression Branch (DRB) and Feature Fusion Branch (FFB). DRB uses multiple convolution layers for feature learning and multi-scale feature extraction. FFB uses attention modules for modeling multi-scale features and element-wise multiply for features fusion. Therefore, AAFM can obtain rich contextual information into the encoder-decoder framework for generating high-quality crowd density maps and accurate counting. We perform experiments on ShanghaiTech, UCF-CC-50, and UCF-QNRF datasets, and AAFM achieves promising results.

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Motion-Attentive Transition for Zero-Shot Video Object Segmentation

Cited by 173 publications

References 38 publications

Classification of Ground-Based Cloud Images by Improved Combined Convolutional Network

Classification of Ground-Based Cloud Images by Improved Combined Convolutional Network

Image Segmentation from Sparse Decomposition with a Pretrained Object-Detection Network

AAFM: Adaptive Attention Fusion Mechanism for Crowd Counting

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