Appearance-based gaze estimation has been very successful with the use of deep learning. Many following works improved domain generalization for gaze estimation. However, even though there has been much progress in domain generalization for gaze estimation, most of the recent work have been focused on cross-dataset performance -- accounting for different distributions in illuminations, head pose, and lighting. Although improving gaze estimation in different distributions of RGB images is important, near-infrared image based gaze estimation is also critical for gaze estimation in dark settings. Also there are inherent limitations relying solely on supervised learning for regression tasks. This paper contributes to solving these problems and proposes GazeCWL, a novel framework for gaze estimation with near-infrared images using contrastive learning. This leverages adversarial attack techniques for data augmentation and a novel contrastive loss function specifically for regression tasks that effectively clusters the features of different samples in the latent space. Our model outperforms previous domain generalization models in infrared image based gaze estimation and outperforms the baseline by 45.6\% while improving the state-of-the-art by 8.6\%, we demonstrate the efficacy of our method.
Global-Graph Guided and Local-Graph Weighted Contrastive Learning for Unified Clustering on Incomplete and Noise Multi-View Data
Guangxi Normal University（广西师范学院）；University of Electronic Science and Technology of China（电子科技大学）；Singapore University of Technology and Design（新加坡科技设计大学）；Hainan University（海南大学）
Towards Fine-grained Visual Representations by Combining Contrastive Learning with Image Reconstruction and Attention-weighted Pooling
Bayer AG（拜耳股份公司）
本文介绍了对比重建，ConRec -一种自监督学习算法，通过联合优化对比和自重建损失来获得图像表示。我们展示了最先进的对比学习方法（例如Simplified）在其表示中捕获细粒度视觉特征方面存在缺陷。ConRec通过添加（1）自我重建任务和（2）对比学习任务中的注意力机制扩展了Simplified框架。这是通过应用具有两个头的简单编码器-解码器架构来实现的。我们表明，这两个扩展有助于改善图像的矢量表示与细粒度的视觉特征。结合这些概念，ConRec在细粒度分类数据集上的性能优于Simplified和Simplified with Attention-Pooling。
University of Illinois at Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；University at Buffalo（布法罗大学）；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）
Fusion of Diffusion Weighted MRI and Clinical Data for Predicting Functional Outcome after Acute Ischemic Stroke with Deep Contrastive Learning
Queens College of the City University of NY（纽约市立大学皇后学院）；National Chung Cheng University（台湾中正大学）；Ditmanson Medical Foundation Chia-Yi Christian Hospital（戴德森医疗财团法人嘉义基督教医院）
Mining Gaze for Contrastive Learning toward Computer-Assisted Diagnosis
School of Biomedical Engineering, Shanghai Jiao Tong University（上海交通大学生物医学工程学院）；Shanghai United Imaging Intelligence Co., Ltd.（上海联影智能医疗科技有限公司）；Shanghai Clinical Research and Trial Center（上海临床研究与试验中心）
Differential Contrastive Training for Gaze Estimation
Beijing Key Laboratory of Traffic Data Mining and Embodied Intelligence（北京交通大数据挖掘与具身智能重点实验室）；Beijing Jiaotong University（北京交通大学）；School of Computer Science and Technology（计算机科学与技术学院）
Tumor Location-weighted MRI-Report Contrastive Learning: A Framework for Improving the Explainability of Pediatric Brain Tumor Diagnosis
The Hospital for Sick Children（病童医院）；University of Toronto（多伦多大学）；Vector Institute for Artificial Intelligence（矢量人工智能研究所）；University Hospital Augsburg（奥格斯堡大学医院）
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）
University of Oxford（牛津大学）；University College London（伦敦大学学院）；eBay；Nanjing University of Aeronautics and Astronautics（南京航空航天大学）；Inception Institute of Artificial Intelligence（起源人工智能研究院）
Gaze on the Prize: Shaping Visual Attention with Return-Guided Contrastive Learning
Department of Computer Science University of California Davis（加州大学戴维斯分校计算机科学系）；Department of Electrical Engineering and Computer Sciences University of California Berkeley（加州大学伯克利分校电气工程与计算机科学系）；Department of Mechanical and Aerospace Engineering University of California Davis（加州大学戴维斯分校机械与航空航天工程系）
Gaze on the Prize通过回报引导的对比学习，提升视觉强化学习的样本效率，解决ManiSkill3基准中难以学习的任务。
Contrastive Learning for Weakly Supervised Phrase Grounding
University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；NVIDIA（英伟达）；Bar Ilan University（巴伊兰大学）
短语基础，将图像区域与字幕词相关联的问题，是视觉语言任务的重要组成部分。我们表明，短语接地可以通过优化字区域的注意，以最大限度地提高图像和字幕词之间的互信息的下限。给定成对的图像和字幕，我们最大限度地提高了注意力加权区域和相应字幕中的单词的兼容性，相比于非对应的图像和字幕对。其核心思想是通过语言模型指导的词替换来构造有效的负面字幕。与来自训练数据的随机采样负数相比，使用我们的负数进行训练会在准确性方面产生$\sim10\%$绝对增益。我们在COCO-Captions上训练的弱监督短语接地模型显示了$5.7\%$的健康增益，以在Flickr 30 K Entities基准上实现$76.7\%$准确性。
Towards Enhancing Time Series Contrastive Learning: A Dynamic Bad Pair Mining Approach
Institute of Data Science, National University of Singapore（新加坡国立大学数据科学研究所）；ByteDance（字节跳动）；National Institute of Health Data Science, Peking University（北京大学国家健康数据科学研究院）
Negatives-Dominant Contrastive Learning for Generalization in Imbalanced Domains
College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics（南京航空航天大学计算机科学与技术学院）；School of Computer and Artificial Intelligence, Nanjing University of Finance and Economics（南京财经大学计算机与人工智能学院）；MIIT Key Laboratory of Pattern Analysis and Machine Intelligence（信息产业部模式分析与机器智能重点实验室）
PROTOCOL: Partial Optimal Transport-enhanced Contrastive Learning for Imbalanced Multi-view Clustering
Shanghai Key Laboratory of Intelligent Information Processing, College of Computer Science and Artificial Intelligence, Fudan University, China（上海智能信息处理实验室，计算机科学与人工智能学院，复旦大学）；College of Computer Science and Technology, Qingdao University, China（计算机科学与技术学院，青岛大学）；Shanghai Key Laboratory of Navigation and Location-based Services, School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, China（上海导航与位置服务实验室，电子信息与电气工程学院，上海交通大学）；Institute of Science and Technology for Brain-Inspired Intelligence and Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Fudan University, China（脑启发智能科学技术研究院和计算神经科学与脑启发智能重点实验室，复旦大学）
FedTGP: Trainable Global Prototypes with Adaptive-Margin-Enhanced Contrastive Learning for Data and Model Heterogeneity in Federated Learning
Shanghai Jiao Tong University（上海交通大学）；Institute for AI Industry Research, Tsinghua University（清华大学智能产业研究院）；Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）；Queen’s University Belfast（英国女王大学）
