Deep Salient Object Detection Research Articles

A saliency-guided street view image inpainting framework for efficient last-meters wayfinding

Global Positioning Systems (GPS) have played a crucial role in various navigation applications. Nevertheless, localizing the perfect destination within the last few meters remains an important but unresolved problem. Limited by the GPS positioning accuracy, navigation systems always show users a vicinity of a destination, but not its exact location. Street view images (SVI) in maps as an immersive media technology have served as an aid to provide the physical environment for human last-meters wayfinding. However, due to the large diversity of geographic context and acquisition conditions, the captured SVI always contains various distracting objects (e.g., pedestrians and vehicles), which will distract human visual attention from efficiently finding the destination in the last few meters. To address this problem, we highlight the importance of reducing visual distraction in image-based wayfinding by proposing a saliency-guided image inpainting framework. It aims at redirecting human visual attention from distracting objects to destination-related objects for more efficient and accurate wayfinding in the last meters. Specifically, a context-aware distracting object detection method driven by deep salient object detection has been designed to extract distracting objects from three semantic levels in SVI. Then we employ a large-mask inpainting method with fast Fourier convolutions to remove the detected distracting objects. Experimental results with both qualitative and quantitative analysis show that our saliency-guided inpainting method11Codes are available at https://github.com/cbhu523/saliency_last_way_finding/. can not only achieve great perceptual quality in street view images but also redirect the human’s visual attention to focus more on static location-related objects than distracting ones. The human-based evaluation also justified the effectiveness of our method in improving the efficiency of locating the target destination.

Engaging Part-Whole Hierarchies and Contrast Cues for Salient Object Detection

Real-world scenes always exhibit objects with clutter backgrounds, posing great challenges for deep salient object detection models. In this paper, we propose salient object detection by engaging two saliency cues, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.e.</i> , the part-whole hierarchies and contrast cues, resulting in a PWHCNet. Specifically, two branches, which consists of a Dynamic Grouping Capsules (DGC) branch and a DenseHRNet branch, are put in place to learn the part-whole hierarchies and contrast cues, respectively. Moreover, to help highlight the whole salient object in complex scenes, a Background Suppression (BS) module is proposed to guide the shallow features of DenseHRNet with the aid of the part-whole relational cues captured by DGC. Subsequently, these two saliency cues are integrated via a Self-Channel and Mutual-Spatial (SCMS) attention mechanism. Experimental results on five benchmarks demonstrate that the proposed PWHCNet achieves state-of-the-art performance while obtaining the whole salient objects with fine details.

Semi-supervised Active Salient Object Detection

In this paper, we propose a novel semi-supervised active salient object detection (SOD) method that actively acquires a small subset of the most discriminative and representative samples for labeling. Two main contributions have been made to prevent the method from being overwhelmed by labeling similar distributed samples. First, we design a saliency encoder-decoder with adversarial discriminator to generate a confidence map, representing the network uncertainty on the current prediction. Then, we select the least confident (discriminative) samples from the unlabeled pool to form the “candidate labeled pool”. Second, we train a Variational Auto-Encoder (VAE) to select and add the most representative data from the “candidate labeled pool” into the labeled pool by comparing their corresponding features in the latent space. Within our framework, these two networks are optimized conditioned on the states of each other progressively. Experimental results on six benchmarking SOD datasets demonstrate that our annotation-efficient learning based salient object detection method, reaching to 14% labeling budget, can be on par with the state-of-the-art fully-supervised deep SOD models. The source code is publicly available via our project page: https://github.com/JingZhang617/Semi-sup-active-self-sup-Learning.

Salient Object Detection in the Deep Learning Era: An In-Depth Survey.

As an essential problem in computer vision, salient object detection (SOD) has attracted an increasing amount of research attention over the years. Recent advances in SOD are predominantly led by deep learning-based solutions (named deep SOD). To enable in-depth understanding of deep SOD, in this paper, we provide a comprehensive survey covering various aspects, ranging from algorithm taxonomy to unsolved issues. In particular, we first review deep SOD algorithms from different perspectives, including network architecture, level of supervision, learning paradigm, and object-/instance-level detection. Following that, we summarize and analyze existing SOD datasets and evaluation metrics. Then, we benchmark a large group of representative SOD models, and provide detailed analyses of the comparison results. Moreover, we study the performance of SOD algorithms under different attribute settings, which has not been thoroughly explored previously, by constructing a novel SOD dataset with rich attribute annotations covering various salient object types, challenging factors, and scene categories. We further analyze, for the first time in the field, the robustness of SOD models to random input perturbations and adversarial attacks. We also look into the generalization and difficulty of existing SOD datasets. Finally, we discuss several open issues of SOD and outline future research directions. All the saliency prediction maps, our constructed dataset with annotations, and codes for evaluation are publicly available at https://github.com/wenguanwang/SODsurvey.

Are RGB-based salient object detection methods unsuitable for light field data?

Considering the significant progress made on RGB-based deep salient object detection (SOD) methods, this paper seeks to bridge the gap between those 2D methods and 4D light field data, instead of implementing specific 4D methods. We observe that the performance of 2D methods changes dramatically with the input refocusing on different depths. This paper attempts to make the 2D methods available for light field SOD by learning to select the best single image from the 4D tensor. Given a 2D method, a deep model is proposed to explicitly compare pairs of SOD results on one light field sample. Moreover, a comparator module is designed to integrate the features from a pair, which provides more discriminative representations to classify. Experiments over 13 latest 2D methods and 2 datasets demonstrate the proposed method can bring about 24.0% and 5.3% average improvement of mean absolute error and F-measure, and outperform state-of-the-art 4D methods by a large margin.

Salient Object Detection by Fusing Local and Global Contexts

Benefiting from the powerful discriminative feature learning capability of convolutional neural networks (CNNs), deep learning techniques have achieved remarkable performance improvement for the task of salient object detection (SOD) in recent years. However, most existing deep SOD models do not fully exploit informative contextual features, which often leads to suboptimal detection performance in the presence of a cluttered background. This paper presents a context-aware attention module that detects salient objects by simultaneously constructing connections between each image pixel and its local and global contextual pixels. Specifically, each pixel and its neighbors bidirectionally exchange semantic information by computing their correlation coefficients, and this process aggregates contextual attention features both locally and globally. In addition, an attention-guided hierarchical network architecture is designed to capture fine-grained spatial details by transmitting contextual information from deeper to shallower network layers in a top-down manner. Extensive experiments on six public SOD datasets show that our proposed model demonstrates superior SOD performance against most of the current state-of-the-art models under different evaluation metrics.

Webly-supervised learning for salient object detection

End-to-end training of a deep CNN-Based model for salient object detection usually requires a huge number of training samples with pixel-level annotations, which are costly and time-consuming to obtain. In this paper, we propose an approach that can utilize large amounts of web data for learning a deep salient object detection model. With thousands of images collected from the Web, we first employ several bottom-up saliency detection techniques to generate salient object masks for all images, and then use a novel quality evaluation method to pick out a subset of images with reliable masks for training. After that, we develop a self-training approach to boost the performance of our initial network, which iterates between the network training process and the training set updating process. Importantly, different from existing webly-supervised or weakly-supervised methods, our approach is able to automatically select reliable images for network training without requiring any human intervention (e.g., dividing images into different difficulty levels).Results of extensive experiments on several widely-used benchmarks demonstrate that our method has achieved state-of-the-art performance. It significantly outperforms existing unsupervised and weakly-supervised salient object detection methods, and achieves competitive or even better performance than fully supervised approaches.

Deep Salient Object Detection With Dense Connections and Distraction Diagnosis

In this paper, we propose two novel components for improving deep salient object detection models. The first component, called saliency detection network (S-Net), introduces dense short- and long-range connections that effectively integrate multiscale features to better exploit contexts at multiple levels. Benefiting from the direct access to low- and high-level features, the S-Net can not only exploit the object context but also preserve the object boundary sharply, leading to enhanced saliency detection performance. Second, a distraction detection network (D-Net) is developed to learn to diagnose which regions of an input image are distracting and harmful for saliency prediction of the S-Net. With such distraction diagnosis, the regions that are distracting to S-Net are removed in hindsight from the input image and the resulted distraction-free image is fed to S-Net for saliency prediction. To train the D-Net, a distraction mining approach is proposed to localize the model-specific distracting regions through examining the sensitiveness of the S-Net to image regions in a principled manner. Besides, the distraction mining approach also provides a way to interpret decisions made by deep neural network (DNN) saliency detection models, which relieves the black-box issues of DNNs to some extent. Extensive experiments on seven popular benchmark datasets demonstrate the effectiveness of the combined S-Net and D-Net, which provides new state of the arts.