Salient Object Detection From Arbitrary Modalities.

Toward desirable saliency prediction, the types and numbers of inputs for a salient object detection (SOD) algorithm may dynamically change in many real-life applications. However, existing SOD algorithms are mainly designed or trained for one particular type of inputs, failing to be generalized to other types of inputs. Consequentially, more types of SOD algorithms need to be prepared in advance for handling different types of inputs, raising huge hardware and research costs.

FMCNet + : Feature-Level Modality Compensation for Visible-Infrared Person Re-Identification.

For visible-infrared person re-identification (VI-ReID), current models that compensate modality-specific information strive to generate missing modality images from existing ones to bridge the cross-modality discrepancies. Despite that, those generated images often suffer from low qualities due to the significant modality gap and include interfering information, e.g.

Middle-Level Feature Fusion for Lightweight RGB-D Salient Object Detection.

Most existing RGB-D salient object detection (SOD) models adopt a two-stream structure to extract the information from the input RGB and depth images. Since they use two subnetworks for unimodal feature extraction and multiple multi-modal feature fusion modules for extracting cross-modal complementary information, these models require a huge number of parameters, thus hindering their real-life applications. To remedy this situation, we propose a novel middle-level feature fusion structure that allows to design a lightweight RGB-D SOD model.

RGB-T Salient Object Detection via Fusing Multi-level CNN Features.

RGB-induced salient object detection has recently witnessed substantial progress, which is attributed to the superior feature learning capability of deep convolutional neural networks (CNNs). However, such detections suffer from challenging scenarios characterized by cluttered backgrounds, low-light conditions and variations in illumination. Instead of improving RGB based saliency detection, this paper takes advantage of the complementary benefits of RGB and thermal infrared images.