mlsanityData to 7 Oct 2026

CalibNet: Dual-branch Cross-modal Calibration for RGB-D Salient Instance Segmentation

arXiv 2307.08098 ↗First posted 16 Jul 2023cs.CVcited by 10

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Abstract (arXiv)

We propose a novel approach for RGB-D salient instance segmentation using a dual-branch cross-modal feature calibration architecture called CalibNet. Our method simultaneously calibrates depth and RGB features in the kernel and mask branches to generate instance-aware kernels and mask features. CalibNet consists of three simple modules, a dynamic interactive kernel (DIK) and a weight-sharing fusion (WSF), which work together to generate effective instance-aware kernels and integrate cross-modal features. To improve the quality of depth features, we incorporate a depth similarity assessment (DSA) module prior to DIK and WSF. In addition, we further contribute a new DSIS dataset, which contains 1,940 images with elaborate instance-level annotations. Extensive experiments on three challenging benchmarks show that CalibNet yields a promising result, i.e., 58.0% AP with 320*480 input size on the COME15K-N test set, which significantly surpasses the alternative frameworks. Our code and dataset are available at: https://github.com/PJLallen/CalibNet.

Key facts. Cited by 10 papers in the map (data through 7 Oct 2026). Builds on 58 of its 90 references that are in the map. First posted on arXiv 16 Jul 2023.

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58 of 90 references.

References · 58

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38 more
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Citing papers per year: 2024: 4; 2025: 3; 2026: 3.

10 papers.