IET Computer Vision最新文献

The past decade has witnessed remarkable achievements in computer vision, owing to the fast development of deep learning. With the advancement of computing power and deep learning algorithms, we can process and apply millions or even hundreds of millions of large-scale data to train robust and advanced deep learning models. In spite of the impressive success, current deep learning methods tend to rely on massive annotated training data and lack the capability of learning from limited exemplars.

However, constructing a million-scale annotated dataset like ImageNet is time-consuming, labour-intensive and even infeasible in many applications. In certain fields, very limited annotated examples can be gathered due to various reasons such as privacy or ethical issues. Consequently, one of the pressing challenges in computer vision is to develop approaches that are capable of learning from limited annotated data. The purpose of this Special Issue is to collect high-quality articles on learning from limited annotations for computer vision tasks (e.g. image classification, object detection, semantic segmentation, instance segmentation and many others), publish new ideas, theories, solutions and insights on this topic and showcase their applications.

In this Special Issue we received 29 papers, all of which underwent peer review. Of the 29 originally submitted papers, 9 have been accepted.

The nine accepted papers can be clustered into two main categories: theoretical and applications. The papers that fall into the first category are by Liu et al., Li et al. and He et al. The second category of papers offers a direct solution to various computer vision tasks. These papers are by Ma et al., Wu et al., Rao et al., Sun et al., Hou et al. and Gong et al. A brief presentation of each of the papers in this Special Issue follows.

All of the papers selected for this Special Issue show that the field of learning from limited annotations for computer vision tasks is steadily moving forward. The possibility of a weakly supervised learning paradigm will remain a source of inspiration for new techniques in the years to come.

Point cloud completion is prevalent due to the insufficient results from current point cloud acquisition equipments, where a large number of point data failed to represent a relatively complete shape. Existing point cloud completion algorithms, mostly encoder-decoder structures with grids transform (also presented as folding operation), can hardly obtain a persuasive representation of input clouds due to the issue that their bottleneck-shape result cannot tell a precise relationship between the global and local structures. For this reason, this article proposes a novel point cloud completion model based on a Stack-Style Folding Network (SSFN). Firstly, to enhance the deep latent feature extraction, SSFN enhances the exploitation of shape feature extractor by integrating both low-level point feature and high-level graphical feature. Next, a precise presentation is obtained from a high dimensional semantic space to improve the reconstruction ability. Finally, a refining module is designed to make a more evenly distributed result. Experimental results shows that our SSFN produces the most promising results of multiple representative metrics with a smaller scale parameters than current models.

Although low-rank representation (LRR)-based subspace learning has been widely applied for feature extraction in computer vision, how to enhance the discriminability of the low-dimensional features extracted by LRR based subspace learning methods is still a problem that needs to be further investigated. Therefore, this paper proposes a novel low-rank preserving embedding regression (LRPER) method by integrating LRR, linear regression, and projection learning into a unified framework. In LRPER, LRR can reveal the underlying structure information to strengthen the robustness of projection learning. The robust metric L_2,1-norm is employed to measure the low-rank reconstruction error and regression loss for moulding the noise and occlusions. An embedding regression is proposed to make full use of the prior information for improving the discriminability of the learned projection. In addition, an alternative iteration algorithm is designed to optimise the proposed model, and the computational complexity of the optimisation algorithm is briefly analysed. The convergence of the optimisation algorithm is theoretically and numerically studied. At last, extensive experiments on four types of image datasets are carried out to demonstrate the effectiveness of LRPER, and the experimental results demonstrate that LRPER performs better than some state-of-the-art feature extraction methods.

Although social robots equipped with visual devices may leak user information, countermeasures for ensuring privacy are not readily available, making visual privacy protection problematic. In this article, a semi-supervised learning algorithm is proposed for visual privacy behaviour recognition based on an improved generative adversarial network for social robots; it is called PBR-GAN. A 9-layer residual generator network enhances the data quality, and a 10-layer discriminator network strengthens the feature extraction. A tailored objective function, loss function, and strategy are proposed to dynamically adjust the learning rate to guarantee high performance. A social robot platform and architecture for visual privacy recognition and protection are implemented. The recognition accuracy of the proposed PBR-GAN is compared with Inception_v3, SS-GAN, and SF-GAN. The average recognition accuracy of the proposed PBR-GAN is 85.91%, which is improved by 3.93%, 9.91%, and 1.73% compared with the performance of Inception_v3, SS-GAN, and SF-GAN respectively. Through a case study, seven situations are considered related to privacy at home, and develop training and test datasets with 8,720 and 1,280 images, respectively, are developed. The proposed PBR-GAN recognises the designed visual privacy information with an average accuracy of 89.91%.

The region proposal network is indispensable to two-stage object detection methods. It generates a fixed number of proposals that are to be classified and regressed by detection heads to produce detection boxes. However, the fixed number of proposals may be too large when an image contains only a few objects but too small when it contains much more objects. Considering this, the authors explored determining a proper number of proposals according to the number of objects in an image to reduce the computational cost while improving the detection accuracy. Since the number of ground truth objects is unknown at the inference stage, the authors designed a simple but effective module to predict the number of foreground regions, which will be substituted for the number of objects for determining the proposal number. Experimental results of various two-stage detection methods on different datasets, including MS-COCO, PASCAL VOC, and CrowdHuman showed that equipping the designed module increased the detection accuracy while decreasing the FLOPs of the detection head. For example, experimental results on the PASCAL VOC dataset showed that applying the designed module to Libra R-CNN and Grid R-CNN increased over 1.5 AP₅₀ while decreasing the FLOPs of detection heads from 28.6 G to nearly 9.0 G.

Temporal event localisation (TEL) has recently attracted increasing attention due to the rapid development of video platforms. Existing methods are based on either fully/weakly supervised or unsupervised learning, and thus they rely on expensive data annotation and time-consuming training. Moreover, these models, which are trained on specific domain data, limit the model generalisation to data distribution shifts. To cope with these difficulties, the authors propose a zero-shot TEL method that can operate without training data or annotations. Leveraging large-scale vision and language pre-trained models, for example, CLIP, we solve the two key problems: (1) how to find the relevant region where the event is likely to occur; (2) how to determine event duration after we find the relevant region. Query guided optimisation for local frame relevance relying on the query-to-frame relationship is proposed to find the most relevant frame region where the event is most likely to occur. Proposal generation method relying on the frame-to-frame relationship is proposed to determine the event duration. The authors also propose a greedy event sampling strategy to predict multiple durations with high reliability for the given event. The authors’ methodology is unique, offering a label-free, training-free, and domain-free approach. It enables the application of TEL purely at the testing stage. The practical results show it achieves competitive performance on the standard Charades-STA and ActivityCaptions datasets.

The one-stage object detector has been widely applied in many computer vision applications due to its high detection efficiency and simple framework. However, one-stage detectors heavily rely on Non-maximum Suppression to remove the duplicated predictions for the same objects, and the detectors produce detection confidence to measure the quality of those predictions. The localisation quality is an important factor to evaluate the predicted bounding boxes, but its role has not been fully utilised in previous works. To alleviate the problem, the Quality Prediction Block (QPB), a lightweight sub-network, is designed by the authors, which strengthens the effect of localisation quality evaluation on detection confidence by leveraging the features of predicted bounding boxes. The QPB is simple in structure and applies to different forms of detection confidence. Extensive experiments are conducted on the public benchmarks, MS COCO, PASCAL VOC and Berkeley DeepDrive. The results demonstrate the effectiveness of our method in the detectors with various forms of detection confidence. The proposed approach also achieves better performance in the stronger one-stage detectors.

Due to the large computational and GPUs memory cost of semantic segmentation, some works focus on designing a lite weight model to achieve a good trade-off between computational cost and accuracy. A common method is to combined CNN and vision transformer. However, these methods ignore the contextual information of multi receptive fields. And existing methods often fail to inject detailed information losses in the downsampling of multi-scale feature. To fix these issues, we propose AG Self-Attention, which is Enhanced Atrous Self-Attention (EASA), and Gate Attention. AG Self-Attention adds the contextual information of multi receptive fields into the global semantic feature. Specifically, the Enhanced Atrous Self-Attention uses weight shared atrous convolution with different atrous rates to get the contextual information under the specific different receptive fields. Gate Attention introduces gating mechanism to inject detailed information into the global semantic feature and filter detailed information by producing “fusion” gate and “update” gate. In order to prove our insight. We conduct numerous experiments in common semantic segmentation datasets, consisting of ADE20 K, COCO-stuff, PASCAL Context, Cityscapes, to show that our method achieves state-of-the-art performance and achieve a good trade-off between computational cost and accuracy.