Conventional RGB-D salient object detection methods aim to leverage depth as complementary information to find the salient regions in both modalities. However, the salient object detection results heavily rely on the quality of captured depth data which sometimes are unavailable. In this work, we make the first attempt to solve the RGB-D salient object detection problem with a novel depth-awareness framework. This framework only relies on RGB data in the testing phase, utilizing captured depth data as supervision for representation learning. To construct our framework as well as achieving accurate salient detection results, we propose a Ubiquitous Target Awareness (UTA) network to solve three important challenges in RGB-D SOD task: 1) a depth awareness module to excavate depth information and to mine ambiguous regions via adaptive depth-error weights, 2) a spatial-aware cross-modal interaction and a channel-aware cross-level interaction, exploiting the low-level boundary cues and amplifying high-level salient channels, and 3) a gated multi-scale predictor module to perceive the object saliency in different contextual scales. Besides its high performance, our proposed UTA network is depth-free for inference and runs in real-time with 43 FPS. Experimental evidence demonstrates that our proposed network not only surpasses the state-of-the-art methods on five public RGB-D SOD benchmarks by a large margin, but also verifies its extensibility on five public RGB SOD benchmarks.
Johns Hopkins University（约翰斯·霍普金斯大学）；Georgia Institute of Technology（佐治亚理工学院）；Université de Montréal（蒙特利尔大学）；Intuitive Surgical Inc.（直观外科公司）
Purpose: We describe a 3D multi-view perception system for the da Vinci surgical system to enable Operating room (OR) scene understanding and context awareness. Methods: Our proposed system is comprised of four Time-of-Flight (ToF) cameras rigidly attached to strategic locations on the daVinci Xi patient side cart (PSC). The cameras are registered to the robot's kinematic chain by performing a one-time calibration routine and therefore, information from all cameras can be fused and represented in one common coordinate frame. Based on this architecture, a multi-view 3D scene semantic segmentati…
Aerospace Information Research Institute, Chinese Academy of Sciences（中国科学院空天信息创新研究院）；University of Chinese Academy of Sciences（中国科学院大学）
Few-shot object detection, expecting detectors to detect novel classes with a few instances, has made conspicuous progress. However, the prototypes extracted by existing meta-learning based methods still suffer from insufficient representative information and lack awareness of query images, which cannot be adaptively tailored to different query images. Firstly, only the support images are involved for extracting prototypes, resulting in scarce perceptual information of query images. Secondly, all pixels of all support images are treated equally when aggregating features into prototype vectors,…
FSANet: Frequency-Spatial Aware Network for Image Segmentation
Delft University of Technology（代尔夫特理工大学）；Southern Medical University（南方医科大学）；Tianjin University（天津大学）；Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）
Segmenting salient objects in an image is an important vision task with ubiquitous applications. The problem becomes more challenging in the presence of a cluttered and textured background, low resolution and/or low contrast images. Even though existing algorithms perform well in segmenting most of the object(s) of interest, they often end up segmenting false positives due to resembling salient objects in the background. In this work, we tackle this problem by iteratively attending to image patches in a recurrent fashion and subsequently enhancing the predicted segmentation mask. Saliency feat…
Nanjing University of Aeronautics and Astronautics（南京航空航天大学）；Shenzhen Research Institute, Nanjing University of Aeronautics and Astronautics（南京航空航天大学深圳研究院）；Hong Kong Polytechnic University（香港理工大学）；Lingnan University（岭南大学）；Hong Kong Metropolitan University（香港都会大学）
Image smoothing is a fundamental low-level vision task that aims to preserve salient structures of an image while removing insignificant details. Deep learning has been explored in image smoothing to deal with the complex entanglement of semantic structures and trivial details. However, current methods neglect two important facts in smoothing: 1) naive pixel-level regression supervised by the limited number of high-quality smoothing ground-truth could lead to domain shift and cause generalization problems towards real-world images; 2) texture appearance is closely related to object semantics, …
TSJNet: A Multi-modality Target and Semantic Awareness Joint-driven Image Fusion Network
School of Physics and Optoelectronic Engineering, Foshan University（物理与光电工程学院，佛山大学）；School of Information Engineering and Automation, Kunming University of Science and Technology（信息工程与自动化学院，昆明理工大学）；School of Artificial Intelligence, Optics and Electronics (i0PEN), School of Computer Science, Northwestern Polytechnical University（人工智能、光学与电子学院（i0PEN），计算机科学学院，西北工业大学）
University of Chinese Academy of Sciences（中国科学院大学）；The University of Sydney（悉尼大学）；Baidu Inc.（百度公司）；Aberystwyth University（阿伯里斯特威斯大学）
Efficient video recognition is a hot-spot research topic with the explosive growth of multimedia data on the Internet and mobile devices. Most existing methods select the salient frames without awareness of the class-specific saliency scores, which neglect the implicit association between the saliency of frames and its belonging category. To alleviate this issue, we devise a novel Temporal Saliency Query (TSQ) mechanism, which introduces class-specific information to provide fine-grained cues for saliency measurement. Specifically, we model the class-specific saliency measuring process as a qu…
Dense depth estimation is essential to scene-understanding for autonomous driving. However, recent self-supervised approaches on monocular videos suffer from scale-inconsistency across long sequences. Utilizing data from the ubiquitously copresent global positioning systems (GPS), we tackle this challenge by proposing a dynamically-weighted GPS-to-Scale (g2s) loss to complement the appearance-based losses. We emphasize that the GPS is needed only during the multimodal training, and not at inference. The relative distance between frames captured through the GPS provides a scale signal that is i…
University of California, Santa Cruz（加州大学圣克鲁兹分校）；NOAA National Ocean Service（美国国家海洋和大气管理局国家海洋局）
This paper presents a machine learning approach for the automatic identification of rip currents with breaking waves. Rip currents are dangerous fast moving currents of water that result in many deaths by sweeping people out to sea. Most people do not know how to recognize rip currents in order to avoid them. Furthermore, efforts to forecast rip currents are hindered by lack of observations to help train and validate hazard models. The presence of web cams and smart phones have made video and still imagery of the coast ubiquitous and provide a potential source of rip current observations. Thes…
This work concerns video-language pre-training and representation learning. In this now ubiquitous training scheme, a model first performs pre-training on paired videos and text (e.g., video clips and accompanied subtitles) from a large uncurated source corpus, before transferring to specific downstream tasks. This two-stage training process inevitably raises questions about the generalization ability of the pre-trained model, which is particularly pronounced when a salient domain gap exists between source and target data (e.g., instructional cooking videos vs. movies). In this paper, we first…
In the single target tracking field, occlusion leads to the loss of tracking targets is a ubiquitous and arduous problem. To solve this problem, we propose a single target tracking algorithm with anti-occlusion capability. The main content of our algorithm is to use the Region Proposal Network to obtain the tracked target and potential interferences, and use the occlusion awareness module to judge whether the interfering object occludes the target. If no occlusion occurs, continue tracking. If occlusion occurs, the prediction module is started, and the motion trajectory of the target in subseq…
