In this paper, we address the Multi-Instance-Learning (MIL) problem when bag labels are naturally represented as ordinal variables (Multi--Instance--Ordinal Regression). Moreover, we consider the case where bags are temporal sequences of ordinal instances. To model this, we propose the novel Multi-Instance Dynamic Ordinal Random Fields (MI-DORF). In this model, we treat instance-labels inside the bag as latent ordinal states. The MIL assumption is modelled by incorporating a high-order cardinality potential relating bag and instance-labels,into the energy function. We show the benefits of the proposed approach on the task of weakly-supervised pain intensity estimation from the UNBC Shoulder-Pain Database. In our experiments, the proposed approach significantly outperforms alternative non-ordinal methods that either ignore the MIL assumption, or do not model dynamic information in target data.
LOMo: Latent Ordinal Model for Facial Analysis in Videos
University of California San Diego（加州大学圣地亚哥分校）；Indian Institute of Technology Kanpur（印度理工学院坎普尔分校）；MPI for Informatics（马克斯·普朗克信息学研究所）；Emotient（Emotient公司）
Scientific Computing and Imaging Institute, University of Utah（犹他大学科学计算与成像研究所）；Kahlert School of Computing, University of Utah（犹他大学卡勒特计算学院）；Department of Biomedical Engineering, University of Utah（犹他大学生物医学工程系）；Nora Eccles Harrison Cardiovascular Research and Training Institute, University of Utah（犹他大学诺拉·埃克尔斯·哈里森心血管研究与培训研究所）；Department of Radiology and Imaging Sciences, University of Utah（犹他大学放射学与影像科学系）；Department of Biomedical Engineering, Johns Hopkins（约翰霍普金斯大学生物医学工程系）；Division of Cardiology, University of Utah（犹他大学心脏病学系）
DRACO: Weakly Supervised Dense Reconstruction And Canonicalization of Objects
Mila（米拉研究所）；Universite de Montreal（蒙特利尔大学）；Brown University（布朗大学）；Robotics Research Center, KCIS, International Institute of Information Technology, Hyderabad（海得拉巴国际信息技术学院KCIS机器人研究中心）
Weakly supervised collective feature learning from curated media
NTT Communication Science Laboratories（NTT通信科学实验室）；University of Cambridge（剑桥大学）；The University of Tokyo（东京大学）；Technical University of Munich（慕尼黑工业大学）；Uber AI Labs（优步人工智能实验室）
Semi-Supervised Gaze Estimation via Disentangled Subspace Contrastive Learning
National Key Laboratory of Fundamental Science on Synthetic Vision, Sichuan University, Chengdu, China（合成视觉基础科学国家重点实验室，四川大学，成都，中国）；College of Computer Science, Sichuan University, Chengdu, China（计算机学院，四川大学，成都，中国）
