In this paper, we aim to develop an efficient and compact deep network for RGB-D salient object detection, where the depth image provides complementary information to boost performance in complex scenarios. Starting from a coarse initial prediction by a multi-scale residual block, we propose a progressively guided alternate refinement network to refine it. Instead of using ImageNet pre-trained backbone network, we first construct a lightweight depth stream by learning from scratch, which can extract complementary features more efficiently with less redundancy. Then, different from the existing fusion based methods, RGB and depth features are fed into proposed guided residual (GR) blocks alternately to reduce their mutual degradation. By assigning progressive guidance in the stacked GR blocks within each side-output, the false detection and missing parts can be well remedied. Extensive experiments on seven benchmark datasets demonstrate that our model outperforms existing state-of-the-art approaches by a large margin, and also shows superiority in efficiency (71 FPS) and model size (64.9 MB).
Recent deep learning-based video salient object detection (VSOD) has achieved some breakthrough, but these methods rely on expensive annotated videos with pixel-wise annotations, weak annotations, or part of the pixel-wise annotations. In this paper, based on the similarities and the differences between VSOD and image salient object detection (SOD), we propose a novel VSOD method via a progressive framework that locates and segments salient objects in sequence without utilizing any video annotation. To use the knowledge learned in the SOD dataset for VSOD efficiently, we introduce dynamic sali…
Beijing Jiaotong University（北京交通大学）；City University of Hong Kong（香港城市大学）；Ningbo University（宁波大学）；City University of Hong Kong Shenzhen Research Institute（香港城市大学深圳研究院）
Fully-supervised salient object detection (SOD) methods have made great progress, but such methods often rely on a large number of pixel-level annotations, which are time-consuming and labour-intensive. In this paper, we focus on a new weakly-supervised SOD task under hybrid labels, where the supervision labels include a large number of coarse labels generated by the traditional unsupervised method and a small number of real labels. To address the issues of label noise and quantity imbalance in this task, we design a new pipeline framework with three sophisticated training strategies. In terms…
Salient object detection plays an important role in many downstream tasks. However, complex real-world scenes with varying scales and numbers of salient objects still pose a challenge. In this paper, we directly address the problem of detecting multiple salient objects across complex scenes. We propose a network architecture incorporating non-local feature information in both the spatial and channel spaces, capturing the long-range dependencies between separate objects. Traditional bottom-up and non-local features are combined with edge features within a feature fusion gate that progressively …
Recent deep learning based salient object detection methods which utilize both saliency and boundary features have achieved remarkable performance. However, most of them ignore the complementarity between saliency features and boundary features, thus get worse predictions in scenes with low contrast between foreground and background. To address this issue, we propose a novel Recurrent Two-Stream Guided Refinement Network (RTGRNet) that consists of iterating Two-Stream Guided Refinement Modules (TGRMs). TGRM consists of a Guide Block and two feature streams: saliency and boundary, the Guide Blo…
We present a simple yet effective progressive self-guided loss function to facilitate deep learning-based salient object detection (SOD) in images. The saliency maps produced by the most relevant works still suffer from incomplete predictions due to the internal complexity of salient objects. Our proposed progressive self-guided loss simulates a morphological closing operation on the model predictions for progressively creating auxiliary training supervisions to step-wisely guide the training process. We demonstrate that this new loss function can guide the SOD model to highlight more complete…
Sun Yat-sen University（中山大学）；SenseTime Group Limited（商汤集团有限公司）
Deep learning based salient object detection has recently achieved great success with its performance greatly outperforms any other unsupervised methods. However, annotating per-pixel saliency masks is a tedious and inefficient procedure. In this paper, we note that superior salient object detection can be obtained by iteratively mining and correcting the labeling ambiguity on saliency maps from traditional unsupervised methods. We propose to use the combination of a coarse salient object activation map from the classification network and saliency maps generated from unsupervised methods as pi…
We solve the problem of salient object detection by investigating how to expand the role of pooling in convolutional neural networks. Based on the U-shape architecture, we first build a global guidance module (GGM) upon the bottom-up pathway, aiming at providing layers at different feature levels the location information of potential salient objects. We further design a feature aggregation module (FAM) to make the coarse-level semantic information well fused with the fine-level features from the top-down pathway. By adding FAMs after the fusion operations in the top-down pathway, coarse-level …
Dalian University of Technology（大连理工大学）；Adobe Research（奥多比研究院）
Deep neural network based methods have made a significant breakthrough in salient object detection. However, they are typically limited to input images with low resolutions ($400\times400$ pixels or less). Little effort has been made to train deep neural networks to directly handle salient object detection in very high-resolution images. This paper pushes forward high-resolution saliency detection, and contributes a new dataset, named High-Resolution Salient Object Detection (HRSOD). To our best knowledge, HRSOD is the first high-resolution saliency detection dataset to date. As another contri…
Nanjing University of Aeronautics and Astronautics（南京航空航天大学）；University of Leicester（莱斯特大学）
Due to the extreme complexity of scale and shape as well as the uncertainty of the predicted location, salient object detection in optical remote sensing images (RSI-SOD) is a very difficult task. The existing SOD methods can satisfy the detection performance for natural scene images, but they are not well adapted to RSI-SOD due to the above-mentioned image characteristics in remote sensing images. In this paper, we propose a novel Attention Guided Network (AGNet) for SOD in optical RSIs, including position enhancement stage and detail refinement stage. Specifically, the position enhancement s…
Texture-guided Saliency Distilling for Unsupervised Salient Object Detection
Sun Yat-sen University（中山大学）；Guangdong Province Key Laboratory of Information Security Technology（广东省信息安全技术重点实验室）；Key Laboratory of Machine Intelligence and Advanced Computing, Ministry of Education（教育部机器智能与先进计算重点实验室）
City University of Hong Kong（香港城市大学）；Beijing Jiaotong University（北京交通大学）
Despite the remarkable advances in visual saliency analysis for natural scene images (NSIs), salient object detection (SOD) for optical remote sensing images (RSIs) still remains an open and challenging problem. In this paper, we propose an end-to-end Dense Attention Fluid Network (DAFNet) for SOD in optical RSIs. A Global Context-aware Attention (GCA) module is proposed to adaptively capture long-range semantic context relationships, and is further embedded in a Dense Attention Fluid (DAF) structure that enables shallow attention cues flow into deep layers to guide the generation of high-leve…
Image-based salient object detection (SOD) has been extensively explored in the past decades. However, SOD on 360$^\circ$ omnidirectional images is less studied owing to the lack of datasets with pixel-level annotations. Toward this end, this paper proposes a 360$^\circ$ image-based SOD dataset that contains 500 high-resolution equirectangular images. We collect the representative equirectangular images from five mainstream 360$^\circ$ video datasets and manually annotate all objects and regions over these images with precise masks with a free-viewpoint way. To the best of our knowledge, it is…
Nanyang Technological University（南洋理工大学）；Beijing Jiaotong University（北京交通大学）；Dalian University of Technology（大连理工大学）；Chinese Academy of Sciences（中国科学院）
We present an effective method to progressively integrate and refine the cross-modality complementarities for RGB-D salient object detection (SOD). The proposed network mainly solves two challenging issues: 1) how to effectively integrate the complementary information from RGB image and its corresponding depth map, and 2) how to adaptively select more saliency-related features. First, we propose a cross-modality feature modulation (cmFM) module to enhance feature representations by taking the depth features as prior, which models the complementary relations of RGB-D data. Second, we propose an…
Beihang University（北京航空航天大学）；State Key Laboratory of Virtual Reality Technology and Systems（虚拟现实技术与系统国家重点实验室）；International Research Institute for Multidisciplinary Science（国际交叉科学研究院）
Image-based salient object detection (SOD) has been extensively studied in the past decades. However, video-based SOD is much less explored since there lack large-scale video datasets within which salient objects are unambiguously defined and annotated. Toward this end, this paper proposes a video-based SOD dataset that consists of 200 videos (64 minutes). In constructing the dataset, we manually annotate all objects and regions over 7,650 uniformly sampled keyframes and collect the eye-tracking data of 23 subjects that free-view all videos. From the user data, we find salient objects in video…
University of Oulu（奥卢大学）；University of Oxford（牛津大学）；Xi’an Jiaotong University（西安交通大学）；The University of Sydney（悉尼大学）；Xiamen University（厦门大学）；University of Technology Sydney（悉尼科技大学）
Recent saliency models extensively explore to incorporate multi-scale contextual information from Convolutional Neural Networks (CNNs). Besides direct fusion strategies, many approaches introduce message-passing to enhance CNN features or predictions. However, the messages are mainly transmitted in two ways, by feature-to-feature passing, and by prediction-to-prediction passing. In this paper, we add message-passing between features and predictions and propose a deep unified CRF saliency model . We design a novel cascade CRFs architecture with CNN to jointly refine deep features and prediction…
Spectrum-driven Mixed-frequency Network for Hyperspectral Salient Object Detection
Beijing Institute of Technology（北京理工大学）；Key Laboratory of Photoelectronic Imaging Technology and System, Ministry of Education of China（教育部光电成像技术与系统重点实验室）；Chongqing Innovation Center（重庆创新中心）
Seamless Detection: Unifying Salient Object Detection and Camouflaged Object Detection
School of Computer Science and Artificial Intelligence, Changzhou University（常州大学计算机科学与人工智能学院）；School of Automation, Northwestern Polytechnical University（西北工业大学自动化学院）；Department of Automation, Tsinghua University（清华大学自动化系）
RA-SOD: Reliability-Aware RGB-T Salient Object Detection under Modality Degradation
State Key Laboratory of Robotics and Systems, Harbin Institute of Technology（哈尔滨工业大学机器人技术与系统国家重点实验室）；School of Computer Science, University of Sydney（悉尼大学计算机科学学院）
Feature matters for salient object detection. Existing methods mainly focus on designing a sophisticated structure to incorporate multi-level features and filter out cluttered features. We present Progressive Feature Polishing Network (PFPN), a simple yet effective framework to progressively polish the multi-level features to be more accurate and representative. By employing multiple Feature Polishing Modules (FPMs) in a recurrent manner, our approach is able to detect salient objects with fine details without any post-processing. A FPM parallelly updates the features of each level by directly…
Referring Camouflaged Object Detection With Multi-Context Overlapped Windows Cross-Attention
School of Computer and Information Engineering, Shanghai Polytechnic University（计算机与信息工程学院，上海理工大学）；Shanghai Key Laboratory of Intelligent Information Processing, School of Computer Science, Fudan University（智能信息处理重点实验室，复旦大学）；Department of Automation, School of Intelligent Manufacturing and Control Engineering, Shanghai Polytechnic University（自动化系，智能制造与控制工程学院，上海理工大学）；Academy for Engineering and Technology, Fudan University（工程与技术学院，复旦大学）；Research and Design Center, Shanghai Insititue Of Computer Technology Company（研发设计中心，上海计算机技术有限公司）
Sun Yat-sen University（中山大学）；The University of Hong Kong（香港大学）
Deep convolutional neural networks have become a key element in the recent breakthrough of salient object detection. However, existing CNN-based methods are based on either patch-wise (region-wise) training and inference or fully convolutional networks. Methods in the former category are generally time-consuming due to severe storage and computational redundancies among overlapping patches. To overcome this deficiency, methods in the second category attempt to directly map a raw input image to a predicted dense saliency map in a single network forward pass. Though being very efficient, it is a…
Grounded Situation Recognition (GSR) aims to generate structured semantic summaries of images for "human-like" event understanding. Specifically, GSR task not only detects the salient activity verb (e.g. buying), but also predicts all corresponding semantic roles (e.g. agent and goods). Inspired by object detection and image captioning tasks, existing methods typically employ a two-stage framework: 1) detect the activity verb, and then 2) predict semantic roles based on the detected verb. Obviously, this illogical framework constitutes a huge obstacle to semantic understanding. First, pre-dete…
School of Electrical and Information Engineering, Jiangsu University, Zhenjiang, 212013, China（江苏大学电气与信息工程学院）；School of Computer Science and Engineering, Jiangsu University of Science and Technology, Zhenjiang, 212003, China（江苏科技大学计算机科学与工程学院）；School of Automation, Southeast University, Nanjing, 210096, China（东南大学自动化学院）；Department of Computer Science and Engineering, University of North Texas, Denton, TX 76207, USA（北卡罗来纳州立大学计算机科学与工程系）；School of Computing, Nanjing Normal University, Nanjing, 210046, Jiangsu（南京师范大学计算机学院）
Progressive Pseudo-Label Optimization for Point-Supervised Change Detection
Dalian Maritime University（大连海事大学）；School of Electronic Information and Artificial Intelligence, Shaanxi University of Science and Technology（陕西科技大学电子信息与人工智能学院）；School of Robotics, Hunan University（湖南大学机器人学院）；Department of Electronic and Electrical Engineering, Brunel University of London（布鲁内尔伦敦大学电子与电气工程系）
SGIFormer: Semantic-guided and Geometric-enhanced Interleaving Transformer for 3D Instance Segmentation
Department of Electrical and Electronic Engineering, The Hong Kong Polytechnic University（电子工程系，香港理工大学）；School of Mechanical Science and Engineering, Huazhong University of Science and Technology（机械科学与工程学院，华中科技大学）
SIGMAE: A Spectral-Index-Guided Foundation Model for Multispectral Remote Sensing
School of Artificial Intelligence, Wuhan University（武汉大学人工智能学院）；School of Artificial Intelligence and Automation, Wuhan University of Science and Technology（武汉科技大学人工智能与自动化学院）；School of Electronic Information, Wuhan University of Science and Technology（武汉科技大学电子信息学院）；School of Computer Science, Wuhan University（武汉大学计算机学院）
University of Central Florida（中佛罗里达大学）；University of the West Indies（西印度群岛大学）
Due to the special gating schemes of Long Short-Term Memory (LSTM), LSTMs have shown greater potential to process complex sequential information than the traditional Recurrent Neural Network (RNN). The conventional LSTM, however, fails to take into consideration the impact of salient spatio-temporal dynamics present in the sequential input data. This problem was first addressed by the differential Recurrent Neural Network (dRNN), which uses a differential gating scheme known as Derivative of States (DoS). DoS uses higher orders of internal state derivatives to analyze the change in information…
DoubleU-NetPlus: A Novel Attention and Context Guided Dual U-Net with Multi-Scale Residual Feature Fusion Network for Semantic Segmentation of Medical Images
United International University（联合国际大学）；Stamford University Bangladesh（孟加拉国斯坦福大学）
National University of Singapore（新加坡国立大学）；Sun Yat-sen University（中山大学）；Adobe Research（奥多比研究院）
In this work, we propose a novel Reversible Recursive Instance-level Object Segmentation (R2-IOS) framework to address the challenging instance-level object segmentation task. R2-IOS consists of a reversible proposal refinement sub-network that predicts bounding box offsets for refining the object proposal locations, and an instance-level segmentation sub-network that generates the foreground mask of the dominant object instance in each proposal. By being recursive, R2-IOS iteratively optimizes the two sub-networks during joint training, in which the refined object proposals and improved segme…
South China University of Technology（华南理工大学）；the Chinese University of Hong Kong (Shenzhen)（香港中文大学（深圳））；USC Institute for Creative Technologies（南加州大学创意技术研究所）
Reconstructing the 3D mesh of a general object from a single image is now possible thanks to the latest advances of deep learning technologies. However, due to the nontrivial difficulty of generating a feasible mesh structure, the state-of-the-art approaches often simplify the problem by learning the displacements of a template mesh that deforms it to the target surface. Though reconstructing a 3D shape with complex topology can be achieved by deforming multiple mesh patches, it remains difficult to stitch the results to ensure a high meshing quality. In this paper, we present an end-to-end si…
University of Tehran（德黑兰大学）；CNRS – Université Toulouse 3（法国国家科学研究中心-图卢兹第三大学）
Previous studies have shown that spike-timing-dependent plasticity (STDP) can be used in spiking neural networks (SNN) to extract visual features of low or intermediate complexity in an unsupervised manner. These studies, however, used relatively shallow architectures, and only one layer was trainable. Another line of research has demonstrated - using rate-based neural networks trained with back-propagation - that having many layers increases the recognition robustness, an approach known as deep learning. We thus designed a deep SNN, comprising several convolutional (trainable with STDP) and p…
