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Research questionHow can RGB-D salient-object detection remain reliable when depth measurements are missing or corrupted?Missing regions, blurred boundaries, and structural artifacts in depth maps can cause multimodal detectors to make worse predictions than RGB-only systems. The central difficulty is deciding when depth-derived geometry is trustworthy enough to use without amplifying its errors.
Computer Vision
Image & Video Processing
Machine Learning
Multimodal Models
Latest papersRecent research connected to this question, newest first.When Depth Hurts: Reliability-Aware Geometry Distillation for Depth-Free RGB-D Salient Object DetectionThe source studies RGB-D salient object detection using RGB-mask training pairs and a training-time geometry teacher, with the resulting network operating without depth at inference. Results are reported across salient-object detection benchmarks and include transfer beyond the training dataset and sensor domain.research paper · Sep 3, 2026
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