无对齐RGBT显著性目标检测：语义引导的非对称相关网络与统一基准

RGBT Salient Object Detection: A Large-scale Dataset and Benchmark

Salient object detection in complex scenes and environments is a challenging research topic. Most works focus on RGB-based salient object detection, which limits its performance of real-life applications when confronted with adverse conditions such as dark environments and complex backgrounds. Taking advantage of RGB and thermal infrared images becomes a new research direction for detecting salient object in complex scenes recently, as thermal infrared spectrum imaging provides the complementary information and has been applied to many computer vision tasks. However, current research for RGBT …

Multi-interactive Dual-decoder for RGB-thermal Salient Object Detection

RGB-thermal salient object detection (SOD) aims to segment the common prominent regions of visible image and corresponding thermal infrared image that we call it RGBT SOD. Existing methods don't fully explore and exploit the potentials of complementarity of different modalities and multi-type cues of image contents, which play a vital role in achieving accurate results. In this paper, we propose a multi-interactive dual-decoder to mine and model the multi-type interactions for accurate RGBT SOD. In specific, we first encode two modalities into multi-level multi-modal feature representations. T…

Scribble-Supervised RGB-T Salient Object Detection

Salient object detection segments attractive objects in scenes. RGB and thermal modalities provide complementary information and scribble annotations alleviate large amounts of human labor. Based on the above facts, we propose a scribble-supervised RGB-T salient object detection model. By a four-step solution (expansion, prediction, aggregation, and supervision), label-sparse challenge of scribble-supervised method is solved. To expand scribble annotations, we collect the superpixels that foreground scribbles pass through in RGB and thermal images, respectively. The expanded multi-modal labels…

TANet: Transformer-based Asymmetric Network for RGB-D Salient Object Detection

Existing RGB-D SOD methods mainly rely on a symmetric two-stream CNN-based network to extract RGB and depth channel features separately. However, there are two problems with the symmetric conventional network structure: first, the ability of CNN in learning global contexts is limited; second, the symmetric two-stream structure ignores the inherent differences between modalities. In this paper, we propose a Transformer-based asymmetric network (TANet) to tackle the issues mentioned above. We employ the powerful feature extraction capability of Transformer (PVTv2) to extract global semantic info…

学习用于多模态显著目标检测的自适应融合库

Learning Adaptive Fusion Bank for Multi-modal Salient Object Detection

提出一种新颖的自适应融合库（LAFB），结合自适应集成模块与间接交互引导模块，同时处理多模态显著目标检测中的多种复杂挑战，并在多个RGBD和RGBT数据集上取得卓越性能。

利用基于 Transformer 的非对称双边 U-Net 促进显著性目标检测

Boosting Salient Object Detection with Transformer-based Asymmetric Bilateral U-Net

本文提出 ABiU-Net，通过 Transformer 与轻量级 CNN 的非对称双边编解码路径及阶段交互，融合全局上下文与局部细节，提升显著性目标检测性能。