Human affordance learning investigates contextually relevant novel pose prediction such that the estimated pose represents a valid human action within the scene. While the task is fundamental to machine perception and automated interactive navigation agents, the exponentially large number of probable pose and action variations make the problem challenging and non-trivial. However, the existing datasets and methods for human affordance prediction in 2D scenes are significantly limited in the literature. In this paper, we propose a novel cross-attention mechanism to encode the scene context for affordance prediction by mutually attending spatial feature maps from two different modalities. The proposed method is disentangled among individual subtasks to efficiently reduce the problem complexity. First, we sample a probable location for a person within the scene using a variational autoencoder (VAE) conditioned on the global scene context encoding. Next, we predict a potential pose template from a set of existing human pose candidates using a classifier on the local context encoding around the predicted location. In the subsequent steps, we use two VAEs to sample the scale and deformation parameters for the predicted pose template by conditioning on the local context and template class. Our experiments show significant improvements over the previous baseline of human affordance injection into complex 2D scenes.
Audio-Visual Segmentation by Exploring Cross-Modal Mutual Semantics
University of Technology Sydney（悉尼科技大学）；The University of Queensland（昆士兰大学）；Futureverse；The Hong Kong University of Science and Technology（香港科技大学）；CSIRO DATA61（联邦科学与工业研究组织数据61研究所）；Netease Fuxi AI Lab（网易伏羲人工智能实验室）
Reasoning About Human-Object Interactions Through Dual Attention Networks
University of California, Berkeley（加州大学伯克利分校）；MIT-IBM Watson AI Lab, IBM Research（麻省理工学院-IBM沃森人工智能实验室，IBM研究院）；Massachusetts Institute of Technology（麻省理工学院）；The Chinese University of Hong Kong（香港中文大学）
Diff-IP2D: Diffusion-Based Hand-Object Interaction Prediction on Egocentric Videos
IRMV Lab, the Department of Automation, Shanghai Jiao Tong University, Shanghai 200240, China（IRMV实验室，自动化系，上海交通大学，上海200240，中国）；College of Intelligence Science and Technology, National University of Defense Technology, Changsha 410073, China（智能科学与技术学院，国防科技大学，长沙410073，中国）；Department of Electronic Engineering, Shanghai Jiao Tong University, Shanghai 200240, China（电子工程系，上海交通大学，上海200240，中国）
MobileGeo: Exploring Hierarchical Knowledge Distillation for Resource-Efficient Cross-view Drone Geo-Localization
Department of Computer and Information Science, University of Macau（计算机与信息科学系，澳门大学）；School of Computer Science and Engineering, South China University of Technology and Pazhou Lab（华南理工大学计算机科学与工程学院及琶洲实验室）；Faculty of Intelligent Manufacturing, Wuyi University（翁源大学智能制造学院）；Unmanned System Research Institute, Northwestern Polytechnical University（无人系统研究院，西北工业大学）
