We describe a method for performing active localization of objects in instances of visual situations. A visual situation is an abstract concept---e.g., "a boxing match", "a birthday party", "walking the dog", "waiting for a bus"---whose image instantiations are linked more by their common spatial and semantic structure than by low-level visual similarity. Our system combines given and learned knowledge of the structure of a particular situation, and adapts that knowledge to a new situation instance as it actively searches for objects. More specifically, the system learns a set of probability distributions describing spatial and other relationships among relevant objects. The system uses those distributions to iteratively sample object proposals on a test image, but also continually uses information from those object proposals to adaptively modify the distributions based on what the system has detected. We test our approach's ability to efficiently localize objects, using a situation-specific image dataset created by our group. We compare the results with several baselines and variations on our method, and demonstrate the strong benefit of using situation knowledge and active context-driven localization. Finally, we contrast our method with several other approaches that use context as well as active search for object localization in images.
Portland State University（波特兰州立大学）；Santa Fe Institute（圣塔菲研究所）
We devise an algorithm using a Bayesian optimization framework in conjunction with contextual visual data for the efficient localization of objects in still images. Recent research has demonstrated substantial progress in object localization and related tasks for computer vision. However, many current state-of-the-art object localization procedures still suffer from inaccuracy and inefficiency, in addition to failing to provide a principled and interpretable system amenable to high-level vision tasks. We address these issues with the current research. Our method encompasses an active search pr…
Fundación Universitaria Konrad Lorenz（康拉德·洛伦兹大学基金会）；University of Illinois at Urbana Champaign（伊利诺伊大学厄巴纳-香槟分校）
We present an active detection model for localizing objects in scenes. The model is class-specific and allows an agent to focus attention on candidate regions for identifying the correct location of a target object. This agent learns to deform a bounding box using simple transformation actions, with the goal of determining the most specific location of target objects following top-down reasoning. The proposed localization agent is trained using deep reinforcement learning, and evaluated on the Pascal VOC 2007 dataset. We show that agents guided by the proposed model are able to localize a sing…
University of California, Santa Barbara（加州大学圣塔芭芭拉分校）；Mitsubishi Electric Research Laboratories（三菱电机研究实验室）
Active learning - a class of algorithms that iteratively searches for the most informative samples to include in a training dataset - has been shown to be effective at annotating data for image classification. However, the use of active learning for object detection is still largely unexplored as determining informativeness of an object-location hypothesis is more difficult. In this paper, we address this issue and present two metrics for measuring the informativeness of an object hypothesis, which allow us to leverage active learning to reduce the amount of annotated data needed to achieve a …
Boston University（波士顿大学）；MIT Lincoln Laboratory（麻省理工学院林肯实验室）
We present a novel efficient object detection and localization framework based on the probabilistic bisection algorithm. A Convolutional Neural Network (CNN) is trained and used as a noisy oracle that provides answers to input query images. The responses along with error probability estimates obtained from the CNN are used to update beliefs on the object location along each dimension. We show that querying along each dimension achieves the same lower bound on localization error as the joint query design. Finally, we compare our approach to the traditional sliding window technique on a real wor…
Portland State University（波特兰州立大学）；Santa Fe Institute（圣塔菲研究所）
A major goal of computer vision is to enable computers to interpret visual situations---abstract concepts (e.g., "a person walking a dog," "a crowd waiting for a bus," "a picnic") whose image instantiations are linked more by their common spatial and semantic structure than by low-level visual similarity. In this paper, we propose a novel method for prior learning and active object localization for this kind of knowledge-driven search in static images. In our system, prior situation knowledge is captured by a set of flexible, kernel-based density estimations---a situation model---that represen…
This paper introduces an active object detection and localization framework that combines a robust untextured object detection and 3D pose estimation algorithm with a novel next-best-view selection strategy. We address the detection and localization problems by proposing an edge-based registration algorithm that refines the object position by minimizing a cost directly extracted from a 3D image tensor that encodes the minimum distance to an edge point in a joint direction/location space. We face the next-best-view problem by exploiting a sequential decision process that, for each step, selects…
The problem of computing category agnostic bounding box proposals is utilized as a core component in many computer vision tasks and thus has lately attracted a lot of attention. In this work we propose a new approach to tackle this problem that is based on an active strategy for generating box proposals that starts from a set of seed boxes, which are uniformly distributed on the image, and then progressively moves its attention on the promising image areas where it is more likely to discover well localized bounding box proposals. We call our approach AttractioNet and a core component of it is …
Shandong University（山东大学）；Peking University（北京大学）；Tencent AI Lab（腾讯人工智能实验室）；Stanford University（斯坦福大学）；vjia（3vjia）
In this work, we tackle the problem of active camera localization, which controls the camera movements actively to achieve an accurate camera pose. The past solutions are mostly based on Markov Localization, which reduces the position-wise camera uncertainty for localization. These approaches localize the camera in the discrete pose space and are agnostic to the localization-driven scene property, which restricts the camera pose accuracy in the coarse scale. We propose to overcome these limitations via a novel active camera localization algorithm, composed of a passive and an active localizati…
Visual event perception tasks such as action localization have primarily focused on supervised learning settings under a static observer, i.e., the camera is static and cannot be controlled by an algorithm. They are often restricted by the quality, quantity, and diversity of \textit{annotated} training data and do not often generalize to out-of-domain samples. In this work, we tackle the problem of active action localization where the goal is to localize an action while controlling the geometric and physical parameters of an active camera to keep the action in the field of view without trainin…
Zhejiang University（浙江大学）；Beijing University of Technology（北京工业大学）；Tsinghua University（清华大学）；Ningbo Global Innovation Center, Zhejiang University（浙江大学宁波全球创新中心）；Jinhua Institute of Zhejiang University（浙江大学金华研究院）
We provide a novel search technique, which uses a hierarchical model and a mutual information gain heuristic to efficiently prune the search space when localizing faces in images. We show exponential gains in computation over traditional sliding window approaches, while keeping similar performance levels.
Faculty of Engineering, University of Fukui（福井大学工学部）
Pole-like landmark has received increasing attention as a domain-invariant visual cue for visual robot self-localization across domains (e.g., seasons, times of day, weathers). However, self-localization using pole-like landmarks can be ill-posed for a passive observer, as many viewpoints may not provide any pole-like landmark view. To alleviate this problem, we consider an active observer and explore a novel "domain-invariant" next-best-view (NBV) planner that attains consistent performance over different domains (i.e., maintenance-free), without requiring the expensive task of training data …
Visual localization is an attractive problem that estimates the camera localization from database images based on the query image. It is a crucial task for various applications, such as autonomous vehicles, assistive navigation and augmented reality. The challenging issues of the task lie in various appearance variations between query and database images, including illumination variations, dynamic object variations and viewpoint variations. In order to tackle those challenges, Panoramic Annular Localizer into which panoramic annular lens and robust deep image descriptors are incorporated is pr…
Temporal Action Localization (TAL) aims to predict both action category and temporal boundary of action instances in untrimmed videos, i.e., start and end time. Fully-supervised solutions are usually adopted in most existing works, and proven to be effective. One of the practical bottlenecks in these solutions is the large amount of labeled training data required. To reduce expensive human label cost, this paper focuses on a rarely investigated yet practical task named semi-supervised TAL and proposes an effective active learning method, named AL-STAL. We leverage four steps for actively selec…
Portland State University（波特兰州立大学）；Santa Fe Institute（圣菲研究所）
We describe a novel architecture for semantic image retrieval---in particular, retrieval of instances of visual situations. Visual situations are concepts such as "a boxing match," "walking the dog," "a crowd waiting for a bus," or "a game of ping-pong," whose instantiations in images are linked more by their common spatial and semantic structure than by low-level visual similarity. Given a query situation description, our architecture---called Situate---learns models capturing the visual features of expected objects as well the expected spatial configuration of relationships among objects. Gi…
Tokyo Institute of Technology（东京工业大学）；Olympus Corporation（奥林巴斯公司）
In this paper we introduce a new camera localization strategy designed for image sequences captured in challenging industrial situations such as industrial parts inspection. To deal with peculiar appearances that hurt standard 3D reconstruction pipeline, we exploit pre-knowledge of the scene by selecting key frames in the sequence (called as anchors) which are roughly connected to a certain location. Our method then seek the location of each frame in time-order, while recursively updating an augmented 3D model which can provide current camera location and surrounding 3D structure. In an experi…
Landmark-based robot self-localization has recently garnered interest as a highly-compressive domain-invariant approach for performing visual place recognition (VPR) across domains (e.g., time of day, weather, and season). However, landmark-based self-localization can be an ill-posed problem for a passive observer (e.g., manual robot control), as many viewpoints may not provide an effective landmark view. In this study, we consider an active self-localization task by an active observer and present a novel reinforcement learning (RL)-based next-best-view (NBV) planner. Our contributions are as …
Active Adversarial Noise Suppression for Image Forgery Localization
College of Electronic and Information Engineering, Shenzhen University（深圳大学电子与信息工程学院）；Shenzhen Key Laboratory of Media Security（深圳媒体安全重点实验室）；Guangdong Laboratory of Machine Perception and Intelligent Computing, Faculty of Engineering, Shenzhen MSU-BIT University（广东机器感知与智能计算实验室，深圳MSU-BIT大学工程学院）
Cell segmentation in microscopy is a challenging problem, since cells are often asymmetric and densely packed. This becomes particularly challenging for extremely large images, since manual intervention and processing time can make segmentation intractable. In this paper, we present an efficient and highly parallel formulation for symmetric three-dimensional (3D) contour evolution that extends previous work on fast two-dimensional active contours. We provide a formulation for optimization on 3D images, as well as a strategy for accelerating computation on consumer graphics hardware. The propos…
Image forgery localization is a very active and open research field for the difficulty to handle the large variety of manipulations a malicious user can perform by means of more and more sophisticated image editing tools. Here, we propose a localization framework based on the fusion of three very different tools, based, respectively, on sensor noise, patch-matching, and machine learning. The binary masks provided by these tools are finally fused based on some suitable reliability indexes. According to preliminary experiments on the training set, the proposed framework provides often a very goo…
Augmented reality devices have the potential to enhance human perception and enable other assistive functionalities in complex conversational environments. Effectively capturing the audio-visual context necessary for understanding these social interactions first requires detecting and localizing the voice activities of the device wearer and the surrounding people. These tasks are challenging due to their egocentric nature: the wearer's head motion may cause motion blur, surrounding people may appear in difficult viewing angles, and there may be occlusions, visual clutter, audio noise, and bad …
University of Southern California（南加州大学）；Google Inc.（谷歌公司）
An objective understanding of media depictions, such as inclusive portrayals of how much someone is heard and seen on screen such as in film and television, requires the machines to discern automatically who, when, how, and where someone is talking, and not. Speaker activity can be automatically discerned from the rich multimodal information present in the media content. This is however a challenging problem due to the vast variety and contextual variability in the media content, and the lack of labeled data. In this work, we present a cross-modal neural network for learning visual representat…
Agent with Warm Start and Active Termination for Plane Localization in 3D Ultrasound
National-Regional Key Technology Engineering Laboratory for Medical Ultrasound（国家区域医疗超声关键技术工程实验室）；Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging（广东省生物医学测量与超声成像重点实验室）；School of Biomedical Engineering, Health Science Center, Shenzhen University（深圳大学医学部生物医学工程学院）；Department of Computer Science and Engineering, The Chinese University of Hong Kong（香港中文大学计算机科学与工程学系）；Department of Ultrasound, Luohu People’s Hospital（深圳市罗湖区人民医院超声科）
Real-time 6-DoF Pose Estimation by an Event-based Camera using Active LED Markers
Automation and Control Institute (ACIN), TU Wien（维也纳工业大学自动化与控制研究所（ACIN））；Austrian Institute of Technology (AIT)（奥地利技术研究院（AIT））；Prophesee GmbH（Prophesee有限公司）
One of the main requirements of tumor extraction is the annotation and segmentation of tumor boundaries correctly. For this purpose, we present a threefold deep learning architecture. First classifiers are implemented with a deep convolutional neural network(CNN) andsecond a region-based convolutional neural network (R-CNN) is performed on the classified images to localize the tumor regions of interest. As the third and final stage, the concentratedtumor boundary is contoured for the segmentation process by using the Chan-Vesesegmentation algorithm. As the typical edge detection algorithms bas…
