Conditional Controllable Image Fusion
Authors: Bing Cao, Xingxin Xu, Pengfei Zhu, Qilong Wang, Qinghua Hu
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments validate our effectiveness in general fusion tasks across diverse scenarios against the competing methods without additional training. |
| Researcher Affiliation | Academia | Bing Cao1,2 Xingxin Xu3 Pengfei Zhu1 Qilong Wang1 Qinghua Hu1 1College of Intelligence and Computing, Tianjin University, Tianjin, China 2State Key Laboratory of Integrated Services Networks, Xidian University, Xi an, China 3School of New Media and Communication, Tianjin University, Tianjin, China |
| Pseudocode | Yes | Algorithm 1 CCF Input: : i,v Output: : f |
| Open Source Code | Yes | The code is publicly available. https://github.com/jehovahxu/CCF |
| Open Datasets | Yes | For multi-modal image fusion task, we conducted experiments on the LLVIP [41] dataset and referred to the test set outlined in Zhu et al. [34]. For MEF and MFF, our testing procedure followed the test setting in MFFW dataset [42] and MEFB dataset [43], respectively. Additionally, we test our method on the TNO dataset and Harvard medical dataset to assess our method s performance within the multi-modal fusion domain, detailed in App. B and H. |
| Dataset Splits | Yes | For multi-modal image fusion task, we conducted experiments on the LLVIP [41] dataset and referred to the test set outlined in Zhu et al. [34]. For MEF and MFF, our testing procedure followed the test setting in MFFW dataset [42] and MEFB dataset [43], respectively. |
| Hardware Specification | Yes | The experiments are conducted on Huawei Atlas 800 Training Server with CANN and NVIDIA RTX 3090 GPU. |
| Software Dependencies | No | Our method utilizes a pre-trained diffusion model as our foundational model [44]. The experiments are conducted on Huawei Atlas 800 Training Server with CANN and NVIDIA RTX 3090 GPU. The detection model employed is YOLOv5 [48], trained on the LLVIP dataset. |
| Experiment Setup | Yes | Our method utilizes a pre-trained diffusion model as our foundational model [44]. This model was directly applied without any subsequent fine-tuning for specific task requirements during our experiments. The experiments are conducted on Huawei Atlas 800 Training Server with CANN and NVIDIA RTX 3090 GPU. Experimental settings are shown in App. A. |