YCDa: YCbCr Decoupled Attention for Real-time Realistic Camouflaged Object Detection

• URL: http://arxiv.org/abs/2603.01602v1
• Date: Mon, 02 Mar 2026 08:31:20 GMT
• Title: YCDa: YCbCr Decoupled Attention for Real-time Realistic Camouflaged Object Detection
• Authors: PeiHuang Zheng, Yunlong Zhao, Zheng Cui, Yang Li,
• Abstract summary: YCDa is an efficient early-stage feature processing strategy that embeds this "chrominance-luminance decoupling and dynamic attention" principle into modern real-time detectors.<n>YCDa is plug-and-play and can be integrated into existing detectors by simply replacing the first downsampling layer.
• Score: 3.1373048585002254
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Human vision exhibits remarkable adaptability in perceiving objects under camouflage. When color cues become unreliable, the visual system instinctively shifts its reliance from chrominance (color) to luminance (brightness and texture), enabling more robust perception in visually confusing environments. Drawing inspiration from this biological mechanism, we propose YCDa, an efficient early-stage feature processing strategy that embeds this "chrominance-luminance decoupling and dynamic attention" principle into modern real-time detectors. Specifically, YCDa separates color and luminance information in the input stage and dynamically allocates attention across channels to amplify discriminative cues while suppressing misleading color noise. The strategy is plug-and-play and can be integrated into existing detectors by simply replacing the first downsampling layer. Extensive experiments on multiple baselines demonstrate that YCDa consistently improves performance with negligible overhead as shown in Fig. Notably, YCDa-YOLO12s achieves a 112% improvement in mAP over the baseline on COD10K-D and sets new state-of-the-art results for real-time camouflaged object detection across COD-D datasets.

Related papers

• Adversarial Patch Generation for Visual-Infrared Dense Prediction Tasks via Joint Position-Color Optimization [14.358458317718174]
  We propose a joint position-color optimization framework (AP-PCO) for generating adversarial patches in visual-infrared settings.<n>We introduce a crossmodal color adaptation strategy that constrains patch appearance according to infrared grayscale characteristics.<n> experiments on visual-infrared dense prediction tasks demonstrate that the proposed AP-PCO achieves consistently strong attack performance.
  arXiv  Detail & Related papers  (2026-02-27T19:26:17Z)
• IrisNet: Infrared Image Status Awareness Meta Decoder for Infrared Small Targets Detection [92.56025546608699]
  IrisNet is a novel meta-learned framework that adapts detection strategies to the input infrared image status.<n>Our approach establishes a dynamic mapping between infrared image features and entire decoder parameters.<n> Experiments on NUDT-SIRST, NUAA-SIRST, and IRSTD-1K datasets demonstrate the superiority of our IrisNet.
  arXiv  Detail & Related papers  (2025-11-25T13:53:54Z)
• SpikeGen: Decoupled "Rods and Cones" Visual Representation Processing with Latent Generative Framework [53.27177454390712]
  This study seeks to emulate the human visual system by integrating multi-modal visual inputs with modern latent-space generative frameworks.<n>We name it SpikeGen. We evaluate its performance across various spike-RGB tasks, including conditional image and video deblurring, dense frame reconstruction from spike streams, and high-speed scene novel-view synthesis.
  arXiv  Detail & Related papers  (2025-05-23T15:54:11Z)
• WSCIF: A Weakly-Supervised Color Intelligence Framework for Tactical Anomaly Detection in Surveillance Keyframes [3.5516803380598074]
  We propose a lightweight anomaly detection framework based on color features for surveillance video clips in a high sensitivity tactical mission.<n>The method fuses unsupervised KMeans clustering with RGB channel histogram modeling to achieve composite detection of structural anomalies and color mutation signals in key frames.<n>The results show that this method can be effectively used for tactical assassination warning, suspicious object screening and environmental drastic change monitoring with strong deployability and tactical interpretation value.
  arXiv  Detail & Related papers  (2025-05-14T04:24:37Z)
• Adaptive Illumination-Invariant Synergistic Feature Integration in a Stratified Granular Framework for Visible-Infrared Re-Identification [18.221111822542024]
  Visible-Infrared Person Re-Identification (VI-ReID) plays a crucial role in applications such as search and rescue, infrastructure protection, and nighttime surveillance.<n>We propose textbfAMINet, an Adaptive Modality Interaction Network.<n>AMINet employs multi-granularity feature extraction to capture comprehensive identity attributes from both full-body and upper-body images.
  arXiv  Detail & Related papers  (2025-02-28T15:42:58Z)
• Hierarchical Graph Interaction Transformer with Dynamic Token Clustering for Camouflaged Object Detection [57.883265488038134]
  We propose a hierarchical graph interaction network termed HGINet for camouflaged object detection. The network is capable of discovering imperceptible objects via effective graph interaction among the hierarchical tokenized features. Our experiments demonstrate the superior performance of HGINet compared to existing state-of-the-art methods.
  arXiv  Detail & Related papers  (2024-08-27T12:53:25Z)
• ZoomNeXt: A Unified Collaborative Pyramid Network for Camouflaged Object Detection [70.11264880907652]
  Recent object (COD) attempts to segment objects visually blended into their surroundings, which is extremely complex and difficult in real-world scenarios. We propose an effective unified collaborative pyramid network that mimics human behavior when observing vague images and camouflaged zooming in and out. Our framework consistently outperforms existing state-of-the-art methods in image and video COD benchmarks.
  arXiv  Detail & Related papers  (2023-10-31T06:11:23Z)
• Frequency Perception Network for Camouflaged Object Detection [51.26386921922031]
  We propose a novel learnable and separable frequency perception mechanism driven by the semantic hierarchy in the frequency domain.<n>Our entire network adopts a two-stage model, including a frequency-guided coarse localization stage and a detail-preserving fine localization stage.<n>Compared with the currently existing models, our proposed method achieves competitive performance in three popular benchmark datasets.
  arXiv  Detail & Related papers  (2023-08-17T11:30:46Z)
• Degrade is Upgrade: Learning Degradation for Low-light Image Enhancement [52.49231695707198]
  We investigate the intrinsic degradation and relight the low-light image while refining the details and color in two steps. Inspired by the color image formulation, we first estimate the degradation from low-light inputs to simulate the distortion of environment illumination color, and then refine the content to recover the loss of diffuse illumination color. Our proposed method has surpassed the SOTA by 0.95dB in PSNR on LOL1000 dataset and 3.18% in mAP on ExDark dataset.
  arXiv  Detail & Related papers  (2021-03-19T04:00:27Z)
• SFANet: A Spectrum-aware Feature Augmentation Network for Visible-Infrared Person Re-Identification [12.566284647658053]
  We propose a novel spectrum-aware feature augementation network named SFANet for cross-modality matching problem. Learning with grayscale-spectrum images, our model can apparently reduce modality discrepancy and detect inner structure relations. In feature-level, we improve the conventional two-stream network through balancing the number of specific and sharable convolutional blocks.
  arXiv  Detail & Related papers  (2021-02-24T08:57:32Z)

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.