2.1 Generate Human-object proposal 首先使用 Fast R-CNN 生成可能是 human/object 的 proposals,作者选择为每个 HOI category 生成 100 proposals,这些 proposals 被分成2类:human, object。接着这2类 proposals 两两配对,生成许多 human-object proposal pair。 2.2 Human stream, Object stream human-object pro...
Learning human-object interactions by graph parsing neural networks提出利用图卷积神经网络,并将HOI任务视为图结构优化问题。 Learning to detect human-object interactions建立了一个基于人对目标区域和成对交互分支的多流网络。 该多流架构的输入是来自预训练检测器(例如,FPN )和原始图像的预测边界框。 在这种多...
This paper addresses the task of detecting and recognizing human-object interactions (HOI) in images and videos. We introduce the Graph Parsing Neural Network (GPNN), a framework that incorporates structural knowledge while being differentiable end-to-end. For a given scene, GPNN infers a parse ...
Given an input image, the task is to detect all humans and objects' bounding boxes and predict their interactions. Like many existing methods, we use an off-the-shelf detector for detection and focus on the stage of interaction classification. We first use a detector to detect all human boun...
Translating noisy sensory signals to perceptual decisions is critical for successful interactions in complex environments. Learning is known to improve perceptual judgments by filtering external noise and task-irrelevant information. Yet, little is known
It is unknown whether object category learning can be formed purely through domain general learning of natural image structure. Here the authors show that human visual brain responses to objects are well-captured by self-supervised deep neural network models trained without labels, supporting a domain...
Can OOD object detectors learn from foundation models? ECCV, 2024. paper N Navaneeth, Tushar, and Souvik Chakraborty. Can your generative model detect out-of-distribution covariate shift? ECCV, 2024. paper Christiaan Viviers, Amaan Valiuddin, Francisco Caetano, Lemar Abdi, Lena Filatova, Peter de...
(ML) and deep learning (DL) techniques to detect attacks within IoT networks. Additionally, security issues and challenges in the IoT ecosystem are thoroughly explored. The paper’s conclusion emphasizes that current detection methods for IoT fall short of adequately addressing a wide range of ...
geometric deep learning model for generating protein representations. Leveraging single-cell transcriptomics combined with networks of protein–protein interactions, cell type-to-cell type interactions and a tissue hierarchy, PINNACLE generates high-resolution protein representations tailored to each cell type....
From left to right: a neural network takes an uncropped image as input and outputs confidence maps and PAFs; these are then used to detect body parts as local peaks in the confidence maps and score all potential connections between them; on the basis of connection scores, a matching ...