Human-object interaction detection is one of the key issues of scene understanding. It has widespread applications in advanced computer vision technology. However, due to the diversity of human postures, the uncertainty of the shape and size in objects, as well as the complexity of the ...
We analyze in detail the advantages and disadvantages between different paradigms, and creatively propose a cascade multi-scale transformer (CMST). CMST comprises three key components: a shared encoder, a human-object pair decoder, and an interaction decoder. These three components are responsible for...
Pose-aware Multi-level Feature Network for Human Object Interaction Detection 解决的问题:HOI(HumanObjectInteraction,人物交互关系预测) 输入一张图片,预测(人,物,动作)三元组 公开数据集: HICO-DET 包含47,774张图片,包含了600类人物交互行为(使用verb-object对),像骑车,骑马,持电话 117种常见行为,像骑,喂,...
In this work, the ISS (Intrinsic Shape Signatures) [28] detector has been used. The ISS detector determines the relevance of the point based on eigenvalues of the matrix created for the support (local neighborhood) of a specific point. Let X={x1,x2,…,xN}X=x1x2…xN be a support of...
PAMIDeep Cognitive Gate: Resembling Human Cognition for Saliency DetectionKe Yan, et al. PAMIA Highly Efficient Model to Study the Semantics of Salient Object DetectionMing-Ming Cheng, et al. PAMILearning to Detect Salient Object with Multi-source Weak SupervisionHongshuang Zhang, Huchuan Lu, et ...
We adopt a multi-scale attention method to each different layers in the U-net backbone to make the network extract features which focus on the crowds, instead of the background in the images. The attention mechanism and the skip-connections can adjust the weights of feature maps while ...
Official Dataste Toolbox of the paper "[CVPR 2023]NeuralDome: A Neural Modeling Pipeline on Multi-View Human-Object Interactions" and "[CVPR2024]HOI-M3: Capture Multiple Humans and Objects Interaction within Contextual Environment" - Juzezhang/NeuralDome
The overall architecture of the proposed RTS-Net is depicted in Fig.1. It mainly comprises the input section, backbone feature extraction network, feature fusion module, and detection head. Operating as a single-stage object detector, it necessitates only a single forward pass to predict the cla...
Detector responses t(·) constitute the α process. The top-down γ process is aimed at predicting and localizing the cor- responding primitive action (or object), based on context provided by the detected group activity (or primitive action). The bottom-up β process is aimed at inferring ...
Extracting useful features at multiple scales is a crucial task in computer vision. The emergence of deep-learning techniques and the advancements in convolutional neural networks (CNNs) have facilitated effective multiscale feature extraction that resul