UniRGB-IR: A Unified Framework for RGB-Infrared Semantic Tasks via Adapter Tuning

• URL: http://arxiv.org/abs/2404.17360v2
• Date: Mon, 04 Nov 2024 14:47:12 GMT
• Title: UniRGB-IR: A Unified Framework for RGB-Infrared Semantic Tasks via Adapter Tuning
• Authors: Maoxun Yuan, Bo Cui, Tianyi Zhao, Jiayi Wang, Shan Fu, Xingxing Wei,
• Abstract summary: We propose a general and efficient framework called UniRGB-IR to unify RGB-IR semantic tasks. A novel adapter is developed to efficiently introduce richer RGB-IR features into the pre-trained foundation model. Experimental results on various RGB-IR downstream tasks demonstrate that our method can achieve state-of-the-art performance.
• Score: 17.36726475620881
• License:
• Abstract: Semantic analysis on visible (RGB) and infrared (IR) images has gained attention for its ability to be more accurate and robust under low-illumination and complex weather conditions. Due to the lack of pre-trained foundation models on the large-scale infrared image datasets, existing methods prefer to design task-specific frameworks and directly fine-tune them with pre-trained foundation models on their RGB-IR semantic relevance datasets, which results in poor scalability and limited generalization. In this work, we propose a general and efficient framework called UniRGB-IR to unify RGB-IR semantic tasks, in which a novel adapter is developed to efficiently introduce richer RGB-IR features into the pre-trained RGB-based foundation model. Specifically, our framework consists of a RGB-based foundation model, a Multi-modal Feature Pool (MFP) module and a Supplementary Feature Injector (SFI) module. The MFP and SFI modules cooperate with each other as an adapter to effectively complement the RGB-based features with the rich RGB-IR features. During training process, we freeze the entire foundation model to inherit prior knowledge and only optimize the proposed adapter. Furthermore, to verify the effectiveness of our framework, we utilize the vanilla vision transformer (ViT-Base) as the pre-trained foundation model to perform extensive experiments. Experimental results on various RGB-IR downstream tasks demonstrate that our method can achieve state-of-the-art performance. The source code and results are available at https://github.com/PoTsui99/UniRGB-IR.git.

Related papers

• Modality Translation for Object Detection Adaptation Without Forgetting Prior Knowledge [11.905387325966311]
  This paper focuses on adapting a large object detection model trained on RGB images to new data extracted from IR images. We propose Modality Translator (ModTr) as an alternative to the common approach of fine-tuning a large model to the new modality.
  arXiv  Detail & Related papers  (2024-04-01T21:28:50Z)
• Tensor Factorization for Leveraging Cross-Modal Knowledge in Data-Constrained Infrared Object Detection [22.60228799622782]
  Key bottleneck in object detection in IR images is lack of sufficient labeled training data. We seek to leverage cues from the RGB modality to scale object detectors to the IR modality, while preserving model performance in the RGB modality. We first pretrain these factor matrices on the RGB modality, for which plenty of training data are assumed to exist and then augment only a few trainable parameters for training on the IR modality to avoid over-fitting.
  arXiv  Detail & Related papers  (2023-09-28T16:55:52Z)
• Residual Spatial Fusion Network for RGB-Thermal Semantic Segmentation [19.41334573257174]
  Traditional methods mostly use RGB images which are heavily affected by lighting conditions, eg, darkness. Recent studies show thermal images are robust to the night scenario as a compensating modality for segmentation. This work proposes a Residual Spatial Fusion Network (RSFNet) for RGB-T semantic segmentation.
  arXiv  Detail & Related papers  (2023-06-17T14:28:08Z)
• CoMAE: Single Model Hybrid Pre-training on Small-Scale RGB-D Datasets [50.6643933702394]
  We present a single-model self-supervised hybrid pre-training framework for RGB and depth modalities, termed as CoMAE. Our CoMAE presents a curriculum learning strategy to unify the two popular self-supervised representation learning algorithms: contrastive learning and masked image modeling.
  arXiv  Detail & Related papers  (2023-02-13T07:09:45Z)
• Mirror Complementary Transformer Network for RGB-thermal Salient Object Detection [16.64781797503128]
  RGB-thermal object detection (RGB-T SOD) aims to locate the common prominent objects of an aligned visible and thermal infrared image pair. In this paper, we propose a novel mirror complementary Transformer network (MCNet) for RGB-T SOD. Experiments on benchmark and VT723 datasets show that the proposed method outperforms state-of-the-art approaches.
  arXiv  Detail & Related papers  (2022-07-07T20:26:09Z)
• Self-Supervised Modality-Aware Multiple Granularity Pre-Training for RGB-Infrared Person Re-Identification [9.624510941236837]
  Modality-Aware Multiple Granularity Learning (MMGL) is a self-supervised pre-training alternative to ImageNet pre-training. MMGL learns better representations (+6.47% Rank-1) with faster training speed (converge in few hours) and solider data efficiency (5% data size) than ImageNet pre-training. Results suggest it generalizes well to various existing models, losses and has promising transferability across datasets.
  arXiv  Detail & Related papers  (2021-12-12T04:40:33Z)
• Self-Supervised Representation Learning for RGB-D Salient Object Detection [93.17479956795862]
  We use Self-Supervised Representation Learning to design two pretext tasks: the cross-modal auto-encoder and the depth-contour estimation. Our pretext tasks require only a few and un RGB-D datasets to perform pre-training, which make the network capture rich semantic contexts. For the inherent problem of cross-modal fusion in RGB-D SOD, we propose a multi-path fusion module.
  arXiv  Detail & Related papers  (2021-01-29T09:16:06Z)
• Siamese Network for RGB-D Salient Object Detection and Beyond [113.30063105890041]
  A novel framework is proposed to learn from both RGB and depth inputs through a shared network backbone. Comprehensive experiments using five popular metrics show that the designed framework yields a robust RGB-D saliency detector. We also link JL-DCF to the RGB-D semantic segmentation field, showing its capability of outperforming several semantic segmentation models.
  arXiv  Detail & Related papers  (2020-08-26T06:01:05Z)
• Data-Level Recombination and Lightweight Fusion Scheme for RGB-D Salient Object Detection [73.31632581915201]
  We propose a novel data-level recombination strategy to fuse RGB with D (depth) before deep feature extraction. A newly lightweight designed triple-stream network is applied over these novel formulated data to achieve an optimal channel-wise complementary fusion status between the RGB and D.
  arXiv  Detail & Related papers  (2020-08-07T10:13:05Z)
• Bi-directional Cross-Modality Feature Propagation with Separation-and-Aggregation Gate for RGB-D Semantic Segmentation [59.94819184452694]
  Depth information has proven to be a useful cue in the semantic segmentation of RGBD images for providing a geometric counterpart to the RGB representation. Most existing works simply assume that depth measurements are accurate and well-aligned with the RGB pixels and models the problem as a cross-modal feature fusion. In this paper, we propose a unified and efficient Crossmodality Guided to not only effectively recalibrate RGB feature responses, but also to distill accurate depth information via multiple stages and aggregate the two recalibrated representations alternatively.
  arXiv  Detail & Related papers  (2020-07-17T18:35:24Z)
• Synergistic saliency and depth prediction for RGB-D saliency detection [76.27406945671379]
  Existing RGB-D saliency datasets are small, which may lead to overfitting and limited generalization for diverse scenarios. We propose a semi-supervised system for RGB-D saliency detection that can be trained on smaller RGB-D saliency datasets without saliency ground truth.
  arXiv  Detail & Related papers  (2020-07-03T14:24:41Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.