Research map / Vision-language and multimodal / Cross-modal learning
Discriminative Multi-Modal Feature Fusion for RGBD Indoor Scene Recognition
Check your idea against 108,967 papers
AI-extracted summary. Written by AI from the paper's title only (no summary yet). Numbers and claims can be wrong or missing context, so check the paper before you cite or build on it.
Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| add the reconstruction term to the discriminative-term-plus-correlation model (ablation) | RGBD multi-modal feature fusion · SUN-RGBD (19 scene classes) | accuracy: 32.8 -> 41.5 | Helped |
| use the full model instead of Wang et al. [19] | RGBD multi-modal feature fusion · SUN-RGBD (19 scene classes) | accuracy: 26.5 -> 41.5 | Helped |
| combine RGB and depth features rather than using RGB features alone | RGBD scene recognition classifiers · SUN-RGBD | accuracy | Helped |
Closest papers by meaning
- DF2Net: Discriminative Feature Learning and Fusion Network for RGB-D Indoor Scene Classification (AAAI 2018 · similarity 0.92)
- Depth CNNs for RGB-D Scene Recognition: Learning from Scratch Better than Transferring from RGB-CNNs (AAAI 2017 · similarity 0.91)
- ACM: Adaptive Cross-Modal Graph Convolutional Neural Networks for RGB-D Scene Recognition (AAAI 2019 · similarity 0.90)
- Modality and Component Aware Feature Fusion For RGB-D Scene Classification (CVPR 2016 · similarity 0.90)
- Locality-Sensitive Deconvolution Networks With Gated Fusion for RGB-D Indoor Semantic Segmentation (CVPR 2017 · similarity 0.88)
- Keep the Balance: A Parameter-Efficient Symmetrical Framework for RGB+X Semantic Segmentation (CVPR 2025 · similarity 0.84)
Writing a paper in this area? scime draft is a free LaTeX editor with AI included, real-time co-authors and a check of your references and venue rules before you submit. It also imports OpenAI Prism projects.