In self-supervised monocular depth estimation, the depth discontinuity and motion objects' artifacts are still challenging problems. Existing self-supervised methods usually utilize a single view to train the depth estimation network. Compared with static views, abundant dynamic properties between video frames are beneficial to refined depth estimation, especially for dynamic objects. In this work, we propose a novel self-supervised joint learning framework for depth estimation using consecutive frames from monocular and stereo videos. The main idea is using an implicit depth cue extractor which leverages dynamic and static cues to generate useful depth proposals. These cues can predict distinguishable motion contours and geometric scene structures. Furthermore, a new high-dimensional attention module is introduced to extract clear global transformation, which effectively suppresses uncertainty of local descriptors in high-dimensional space, resulting in a more reliable optimization in learning framework. Experiments demonstrate that the proposed framework outperforms the state-of-the-art(SOTA) on KITTI and Make3D datasets.
CIEC: Coupling Implicit and Explicit Cues for Multimodal Weakly Supervised Manipulation Localization
School of Computer Science and Engineering（计算机科学与工程学院）；MoE Key Laboratory of Information Technology（信息技术关键实验室）；Key Laboratory of Information Security Technology（信息安全技术重点实验室）；Sun Yat-sen University（中山大学）；State Key Laboratory of Mathematical Engineering and Advanced Computing（数学工程与先进计算国家重点实验室）；Alibaba Group（阿里巴巴集团）
Incremental Joint Learning of Depth, Pose and Implicit Scene Representation on Monocular Camera in Large-scale Scenes
Institute of Medical Robotics and Department of Automation, Shanghai Jiao Tong University（上海交通大学医学机器人研究所和自动化系）；Key Laboratory of System Control and Information Processing, Ministry of Education, Shanghai（系统控制与信息处理重点实验室，教育部，上海）；School of Electrical and Electronic Engineering, Nanyang Technological University（南洋理工大学电子与电气工程学院）
Looking Beyond Visible Cues: Implicit Video Question Answering via Dual-Clue Reasoning
Shanghai Jiao Tong University（上海交通大学）；ZhongguanCun Academy（中关村学院）；Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）；Shanghai University（上海大学）；City University of Hong Kong（香港城市大学）；Wuhan University（武汉大学）
Guidance and Teaching Network for Video Salient Object Detection
Wuhan University（武汉大学）；Jiangxi University of Finance and Economics（江西财经大学）
现有的视频显著目标检测（VSOD）方法由于难以挖掘时空线索，在理解复杂和噪声场景时受到限制，往往无法推断出显著目标。为了减轻这些缺点，我们提出了一个简单而有效的架构，称为指导和教学网络（GTNet），独立地隐藏有效的空间和时间线索与隐式指导和明确的教学功能和决策水平，分别。具体来说，我们（a）引入一个时间调制器来隐式地将来自运动的特征桥接到外观分支中，这能够协同地融合跨模态特征，以及（b）利用运动引导的掩码来在特征聚合期间传播显式提示。这种新的学习策略通过解耦复杂的时空线索和映射信息线索在不同的方式取得了令人满意的结果。在三个具有挑战性的基准测试上进行的大量实验表明，该方法可以在单个TITAN Xp GPU上以约28 fps的速度运行，并与14个尖端基线相比具有竞争力。
Camera-based implicit mind reading by capturing higher-order semantic dynamics of human gaze within environmental context
China University of Petroleum (East China)（中国石油大学（华东））；the Qingdao Institute of Software（青岛软件研究所）；the College of Computer Science and Technology（计算机科学与技术学院）；Shandong Key Laboratory of Intelligent Oil & Gas Industrial Software（山东省智能油气工业软件重点实验室）；College of Information Science and Technology（信息科学与技术学院）
DGSeg: Dynamic Gating of Semantic-Spatial Guided Predictions for Reasoning Segmentation
State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所多模态人工智能系统国家重点实验室）；School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）；Nanjing Artificial Intelligence Research of IA（中国科学院自动化所南京人工智能研究院）
AlignMiF: Geometry-Aligned Multimodal Implicit Field for LiDAR-Camera Joint Synthesis
Shenzhen Campus of Sun Yat-sen University（中山大学深圳校区）；DarkMatter AI Research（DarkMatter AI研究）；University of Oxford（牛津大学）；HKU（香港大学）；Cainiao Group（菜鸟集团）；NCUT（北方工业大学）；Sun Yat-sen University（中山大学）；Westlake University（西湖大学）
ImplicitCell: Resolution Cell Modeling of Joint Implicit Volume Reconstruction and Pose Refinement in Freehand 3D Ultrasound
School of Information Science and Technology, ShanghaiTech University（上海科技大学信息科学与技术学院）；Tongren Hospital, Shanghai Jiao Tong University School of Medicine（上海交通大学医学院附属同仁医院）
Treating Motion as Option with Output Selection for Unsupervised Video Object Segmentation
School of Electrical and Electronic Engineering, Yonsei University（延世大学电气电子工程学院）；Department of Software Convergence, Kyung Hee University（韩国庆熙大学软件融合系）；EO/IR Systems R&D Lab, LIG Nex1（LIG Nex1 EO/IR系统研发实验室）
NICER-SLAM: Neural Implicit Scene Encoding for RGB SLAM
ETH Zürich（苏黎世联邦理工学院）；MPI for Intelligent Systems（马克斯·普朗克智能系统研究所）；Lund University（隆德大学）；Zhejiang University（浙江大学）；University of Amsterdam（阿姆斯特丹大学）；University of Tübingen（图宾根大学）；Tübingen AI Center（图宾根人工智能中心）；Microsoft（微软）
EPIPTrack: Rethinking Prompt Modeling with Explicit and Implicit Prompts for Multi-Object Tracking
College of Computer Science and Technology, Jilin University（吉林大学计算机科学与技术学院）；Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University（吉林大学教育部长春符号计算与知识工程重点实验室）；Yangtze University（扬子大学）
MetaView: Monocular Novel View Synthesis with Scale-Aware Implicit Geometry Priors
Nanyang Technological University（南洋理工大学）；Kolors Team, Kuaishou Technology（快手科技色彩团队）；The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））
Joint Explicit and Implicit Cross-Modal Interaction Network for Anterior Chamber Inflammation Diagnosis
College of Computer Science & Technology, Zhejiang University（浙江大学计算机科学与技术学院）；State Key Laboratory of Ophthalmology, Sun Yat-sen University（中山大学眼科国家重点实验室）
