Divide-and-Conquer: Confluent Triple-Flow Network for RGB-T Salient Object Detection
Nanjing University of Science and Technology · Hong Kong University of Science and Technology · +1 more institution
Abstract
RGB-Thermal Salient Object Detection (RGB-T SOD) aims to pinpoint prominent objects within aligned pairs of visible and thermal infrared images. A key challenge lies in bridging the inherent disparities between RGB and Thermal modalities for effective saliency map prediction. Traditional encoder-decoder architectures, while designed for cross-modality feature interactions, may not have adequately considered the robustness against noise originating from defective modalities, thereby leading to suboptimal performance in complex scenarios. Inspired by hierarchical human visual systems, we propose the ConTriNet, a robust Confluent Triple-Flow Network employing a "Divide-and-Conquer" strategy. This framework…
Citation impact
- FWCI
- 36.73
- Percentile
- 100%
- References
- 84
Authors
5Topics & keywords
- Computer science
- Divide and conquer algorithms
- Artificial intelligence
- Object detection
- Computer vision
- Salient
- Object (grammar)
- Flow (mathematics)