1,720,981 research outputs found
Reinforcement Learning for Active Visual Perception
Get PDFVisual perception refers to automatically recognizing, detecting, or otherwise sensing the content of an image, video or scene. The most common contemporary approach to tackle a visual perception task is by training a deep neural network on a pre-existing dataset which provides examples of task success and failure, respectively. Despite remarkable recent progress across a wide range of vision tasks, many standard methodologies are static in that they lack mechanisms for adapting to any particular settings or constraints of the task at hand. The ability to adapt is desirable in many practical scenarios, since the operating regime often differs from the training setup. For example, a robot which has learnt to recognize a static set of training images may perform poorly in real-world settings, where it may view objects from unusual angles or explore poorly illuminated environments. The robot should then ideally be able to actively position itself to observe the scene from viewpoints where it is more confident, or refine its perception with only a limited amount of training data for its present operating conditions.In this thesis we demonstrate how reinforcement learning (RL) can be integrated with three fundamental visual perception tasks -- object detection, human pose estimation, and semantic segmentation -- in order to make the resulting pipelines more adaptive, accurate and/or faster. In the first part we provide object detectors with the capacity to actively select what parts of a given image to analyze and when to terminate the detection process. Several ideas are proposed and empirically evaluated, such as explicitly including the speed-accuracy trade-off in the training process, which makes it possible to specify this trade-off during inference. In the second part we consider active multi-view 3d human pose estimation in complex scenarios with multiple people. We explore this in two different contexts: i) active triangulation, which requires carefully observing each body joint from multiple viewpoints, and ii) active viewpoint selection for monocular 3d estimators, which requires considering which viewpoints yield accurate fused estimates when combined. In both settings the viewpoint selection systems face several challenges, such as partial observability resulting e.g. from occlusions. We show that RL-based methods outperform heuristic ones in accuracy, with negligible computational overhead. Finally, the thesis concludes with establishing a framework for embodied visual active learning in the context of semantic segmentation, where an agent should explore a 3d environment and actively query annotations to refine its visual perception. Our empirical results suggest that reinforcement learning can be successfully applied within this framework as well
Aerial View Localization with Reinforcement Learning: Towards Emulating Search-and-Rescue
Get PDFClimate-induced disasters are and will continue to be on the rise, and thus search-and-rescue (SAR) operations, where the task is to localize and assist one or several people who are missing, become increasingly relevant. In many cases the rough location may be known and a UAV can be deployed to explore a given, confined area to precisely localize the missing people. Due to time and battery constraints it is often critical that localization is performed as efficiently as possible. In this work we approach this type of problem by abstracting it as an aerial view goal localization task in a framework that emulates a SAR-like setup without requiring access to actual UAVs. In this framework, an agent operates on top of an aerial image (proxy for a search area) and is tasked with localizing a goal that is described in terms of visual cues. To further mimic the situation on an actual UAV, the agent is not able to observe the search area in its entirety, not even at low resolution, and thus it has to operate solely based on partial glimpses when navigating towards the goal. To tackle this task, we propose AiRLoc, a reinforcement learning (RL)-based model that decouples exploration (searching for distant goals) and exploitation (localizing nearby goals). Extensive evaluations show that AiRLoc outperforms heuristic search methods as well as alternative learnable approaches, and that it generalizes across datasets, e.g. to disaster-hit areas without seeing a single disaster scenario during training. We also conduct a proof-of-concept study which indicates that the learnable methods outperform humans on average. Code and models have been made publicly available at https://github.com/aleksispi/airloc.</p
Deep Reinforcement Learning of Region Proposal Networks for Object Detection
No full textWe propose drl-RPN, a deep reinforcement learning-based visual recognition model consisting of a sequential region proposal network (RPN) and an object detector. In contrast to typical RPNs, where candidate object regions (RoIs) are selected greedily via class-agnostic NMS, drl-RPN optimizes an objective closer to the final detection task. This is achieved by replacing the greedy RoI selection process with a sequential attention mechanism which is trained via deep reinforcement learning (RL). Our model is capable of accumulating class-specific evidence over time, potentially affecting subsequent proposals and classification scores, and we show that such context integration significantly boosts detection accuracy. Moreover, drl-RPN automatically decides when to stop the search process and has the benefit of being able to jointly learn the parameters of the policy and the detector, both represented as deep networks. Our model can further learn to search over a wide range of exploration-accuracy trade-offs making it possible to specify or adapt the exploration extent at test time. The resulting search trajectories are image- and category-dependent, yet rely only on a single policy over all object categories. Results on the MS COCO and PASCAL VOC challenges show that our approach outperforms established, typical state-of-the-art object detection pipelines
Stray Light Compensation in Optical Systems
Get PDFAll optical equipment suffers from a phenomenon called stray light, which is defined as unwanted light in an optical system. Images contaminated by stray light tend to have lower contrast and reduced detail, which motivates the need for reducing it in many applications. This master thesis considers computational stray light compensation in digital cameras. In particular, the purpose is to reduce stray light in surveillance cameras developed by Axis Communications. We follow in the spirit of other digital stray light compensation approaches, in which measurements are fit to a parametric shift-variant point spread function (PSF) describing the stray light characteristics of the optical system. The observed contaminated image is modelled as an underlying ideal image convolved with the PSF. Once the PSF has been determined, a deconvolution is performed to obtain a restored image. We provide comparisons of a few deconvolution strategies and their performances regarding the restoration of images. Also, we discuss different techniques for decreasing the computational cost of the compensation. An experiment in which the images are compared to a ground-truth is proposed to objectively measure performance. The results indicate that the restored images are closer to the ground-truth compared to the observed image, which implies that the stray light compensation is successful.se bilaga
Exact clustering of weighted graphs via semidefinite programming
No full textAs a model problem for clustering, we consider the densest k-disjoint-clique problem of partitioning a weighted complete graph into k disjoint subgraphs such that the sum of the densities of these subgraphs is maximized. We establish that such subgraphs can be recovered from the solution of a particular semidefinite relaxation with high probability if the input graph is sampled from a distribution of clusterable graphs. Specifically, the semidefinite relaxation is exact if the graph consists of k large disjoint subgraphs, corresponding to clusters, with weight concentrated within these subgraphs, plus a moderate number of nodes not belonging to any cluster. Further, we establish that if noise is weakly obscuring these clusters, i.e, the between-cluster edges are assigned very small weights, then we can recover significantly smaller clusters. For example, we show that in approximately sparse graphs, where the between-cluster weights tend to zero as the size n of the graph tends to infinity, we can recover clusters of size polylogarithmic in n under certain conditions on the distribution of edge weights. Empirical evidence from numerical simulations is also provided to support these theoretical phase transitions to perfect recovery of the cluster structure
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Get PDF“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Get PDFWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Get PDFWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
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