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Found 13 papers, 0 papers with code
Review of Visual Saliency Detection with Comprehensive Information
With the acquisition technology development, more comprehensive information, such as depth cue, inter-image correspondence, or temporal relationship, is available to extend image saliency detection to RGBD saliency detection, co-saliency detection, or video saliency detection.
An Iterative Co-Saliency Framework for RGBD Images
In this paper, we propose an iterative RGBD co-saliency framework, which utilizes the existing single saliency maps as the initialization, and generates the final RGBD cosaliency map by using a refinement-cycle model.
Saliency Detection for Stereoscopic Images Based on Depth Confidence Analysis and Multiple Cues Fusion
Stereoscopic perception is an important part of human visual system that allows the brain to perceive depth.
Co-saliency Detection for RGBD Images Based on Multi-constraint Feature Matching and Cross Label Propagation
Different from the most existing co-saliency methods focusing on RGB images, this paper proposes a novel co-saliency detection model for RGBD images, which utilizes the depth information to enhance identification of co-saliency.
Person Re-Identification by Semantic Region Representation and Topology Constraint
In this paper, we propose a novel person re-identification method, which consists of a reliable representation called Semantic Region Representation (SRR), and an effective metric learning with Mapping Space Topology Constraint (MSTC).
HSCS: Hierarchical Sparsity Based Co-saliency Detection for RGBD Images
In this paper, we propose a novel co-saliency detection method for RGBD images based on hierarchical sparsity reconstruction and energy function refinement.
Semi-Heterogeneous Three-Way Joint Embedding Network for Sketch-Based Image Retrieval
How to align abstract sketches and natural images into a common high-level semantic space remains a key problem in SBIR.
Video Compression Coding via Colorization: A Generative Adversarial Network (GAN)-Based Approach
Under the limited storage, computing and network bandwidth resources, the video compression coding technology plays an important role for visual communication.
Novel View Synthesis from a Single Image via Unsupervised learning
In particular, the network consists of a token transformation module (TTM) that facilities the transformation of the features extracted from a source viewpoint image into an intrinsic representation with respect to a pre-defined reference pose and a view generation module (VGM) that synthesizes an arbitrary view from the representation.
Multi-View Stereo with Transformer
It is built upon Transformer and is capable of extracting dense features with global context and 3D consistency, which are crucial to achieving reliable matching for MVS.
Deep Stereo Image Compression via Bi-Directional Coding
Existing learning-based stereo compression methods usually adopt a unidirectional approach to encoding one image independently and the other image conditioned upon the first.
PSNet: Parallel Symmetric Network for Video Salient Object Detection
Finally, we use the Importance Perception Fusion (IPF) module to fuse the features from two parallel branches according to their different importance in different scenarios.
Multi-Projection Fusion and Refinement Network for Salient Object Detection in 360° Omnidirectional Image
Salient object detection (SOD) aims to determine the most visually attractive objects in an image.
