在显著性目标检测(Salient Object Detection, SOD)任务中,receptive fields(感受野)扮演着至关重要的角色。以下是对你的问题的详细回答: 1. 解释receptive fields在显著性目标检测中的重要性 感受野是指卷积神经网络(CNN)中某一层输出特征图上的像素点在输入图像上对应的区域大小。在显著性目标检测中,感受野的大小直接...
Salient Object Detection (SOD): 显著性目标检测,目的为突出图像中的显著性目标区域。 一、动机: 在视觉显著性方面的研究中,主要分为两类任务:眼动预测和显著性目标检测。但是两者之间的关系,却很少被研... 查看原文 Video Salient Object Detection via Fully Convolutional Networks Video Salient Object...
所以提出了:轻量级多级特征差异融合网络(MFDF):首次提出用于实时RGB-D-T SOD的轻量级网络,考虑了不同模态(RGB、深度、热成像)的信息差异。 在光线较弱、较暗或光线不均等复杂条件下,RGB 和深度图像所包含的信息可能不足以进行准确探测。红外热成像仪可以捕捉目标发出的红外辐射来生成图像,使其在夜间或恶劣天气条件...
On this basis, we proposed a fusion model SwinSOD for RGB salient object detection. This model used a Swin-Transformer as the encoder to extract hierarchical features, was driven by a multi-head attention mechanism to bridge the gap between hierarchical features, progressively fused adjacent layer...
【TMM2024】Frequency-Guided Spatial Adaptation for Camouflaged Object Detection 论文链接: https://arxiv.org/abs/2409.12421这个论文研究 Camouflaged Object Detection (COD)问题,作者认为,使用 pretrained foundation model 可以改进COD的准确率,但是当前的 ada… 高峰OUC发表于OUC的搬... MULTI-SCALE CONTEXT AGGRE...
1、Lightweight Multi-Scale Adapter,LMSA 作者认为,SAM编码器的参数过多,同时 SOD训练数据不足会影响网络的全面微调,因此,使用Adaptor可以让SAM应用于SOD,同时,应用多尺度特征提取能够提升性能。LMSA结构如下图所示,本质上就是在 Adpator 里把特征池化成多个尺度分别处理。
A curated list of awesome resources for salient object detection (SOD), focusing more on multi-modal SOD, such as RGB-D SOD. - visionxiang/awesome-salient-object-detection
Here is the SALient Object Detection (SALOD) benchmark (paper link), published in Pattern Recognition. We have re-implemented over 20 SOD methods using the same settings, including input size, data loader and evaluation metrics (thanks toMetrics). Some other networks are debugging now, it is...
Salient-Object-Detectionsu**n^ 上传 Python Shell 显著物体检测(Salient Object Detection,简称SOD)是一种计算机视觉任务,主要用于在图像或视频中识别和突出显示那些相对于周围环境更吸引人或最显眼的区域。该技术通过算法分析图像,增强对人眼最容易被注意到的对象的识别。SOD模型通常利用深度学习方法,如卷积神经网络(...
Salient object detection (SOD) aims to identify standout elements in a scene, with recent advancements primarily focused on integrating depth data (RGB-D) or temporal data from videos to enhance SOD in complex scenes. However, the unison of two types of crucial information remains largely ...