Localization Recall Precision (LRP): A New Performance Metric for Object Detection

Average precision (AP), the area under the recall-precision (RP) curve, is the standard performance measure for object detection. Despite its wide acceptance, it has a number of shortcomings, the most important of which are (i) the inability to distinguish very different RP curves, and (ii) the lack of directly measuring bounding box localization accuracy. In this paper, we propose 'Localization Recall Precision (LRP) Error', a new metric which we specifically designed for object detection. LRP Error is composed of three components related to localization, false negative (FN) rate and false positive (FP) rate. Based on LRP, we introduce the 'Optimal LRP', the minimum achievable LRP error representing the best achievable configuration of the detector in terms of recall-precision and the tightness of the boxes. In contrast to AP, which considers precisions over the entire recall domain, Optimal LRP determines the 'best' confidence score threshold for a class, which balances the trade-off between localization and recall-precision. In our experiments, we show that, for state-of-the-art object (SOTA) detectors, Optimal LRP provides richer and more discriminative information than AP. We also demonstrate that the best confidence score thresholds vary significantly among classes and detectors. Moreover, we present LRP results of a simple online video object detector which uses a SOTA still image object detector and show that the class-specific optimized thresholds increase the accuracy against the common approach of using a general threshold for all classes. At https://github.com/cancam/LRP we provide the source code that can compute LRP for the PASCAL VOC and MSCOCO datasets. Our source code can easily be adapted to other datasets as well.Comment: to appear in ECCV 201

オブジェクト指向モデルからの自動コード生成

Object-oriented methodologies suggest to create different models of a system indicative of its different aspects. Object Modeling Technique (OMT), an object-oriented methodology, represents the static structure of classes in a system by an ER-style diagram called the object model, and the dynamic behavior of the classes by a set of state transition diagrams called the dynamic model. To speed up the software development process, it has been desired to have CASE tools that can automatically generate code from these models. In the present work, an attempt has been made to find methods to automatically generate executable code from the object-oriented models in general and the dynamic model in particular. An object-oriented approach has been proposed to convert a state diagram representing the behavior of a multi-state class into code. States are treated as classes and transitions as their operations. Inheritance is used to implement state hierarchy and object composition is used to implement concurrent states. It is realized that active objects can well be represented by activity diagrams rather than state diagrams. In the proposed approach, active objects are implemented as Java threads. A system, O-Code, has also been developed that implements the proposed method and automatically generates executable Java code from the specifications of the object and dynamic models. A comparison with Rhapsody shows that the code generated by O-Code is much more compact, efficient and understandable than that of Rhapsody.Thesis (Ph. D. in Engineering)--University of Tsukuba, (A), no. 1966, 1998.7.2

Aspects for Graph Grammars

Aspect-oriented programming (AOP) is an extension to the object oriented paradigm that aims to provide better modularity for code that is usually scattered across an object-oriented system such as logging, authentication and distributed object handling. Aspect weaving is a novel way to compose systems, focusing on the integration of system-wide policies through pattern-action rules. While there are several semantic proposals for representing aspects over source code and programs, aspect weaving for visual models such as graph rewriting systems is still not fully established. In this work, we propose the definition of aspect-oriented graph grammars, an extension to conventional graph grammar where aspects are modeled as transformation rules over the structure of a base graph grammar

Laser Guide Star for 3.6m and 8m telescopes: Performances and astrophysical implications

We have constructed an analytical model to simulate the behavior of an adaptive optics system coupled with a sodium laser guide star. The code is applied to a 3.6-m and 8m class telescopes. The results are given in terms of Strehl ratio and full width at half maximum of the point spread function. Two atmospheric models are used, one representing good atmospheric conditions (20 per cent of the time), the other median conditions. Sky coverage is computed for natural guide star and laser guide star systems, with two different methods. The first one is a statistical approach, using stellar densities, to compute the probability to find a nearby reference. The second is a cross-correlation of a science object catalogue and the USNO catalogue. Results are given in terms of percentage of the sky that can be accessed with given performances, and in terms of number of science object that can be observed, with Strehls greater than 0.2 and 0.1 in K and J bands.Comment: 14 pages, 17 figures, accepted for publication in MNRAS. Also available at: http://www-obs.univ-lyon1.fr/~lelouarn

Res2Net: A New Multi-scale Backbone Architecture

Representing features at multiple scales is of great importance for numerous vision tasks. Recent advances in backbone convolutional neural networks (CNNs) continually demonstrate stronger multi-scale representation ability, leading to consistent performance gains on a wide range of applications. However, most existing methods represent the multi-scale features in a layer-wise manner. In this paper, we propose a novel building block for CNNs, namely Res2Net, by constructing hierarchical residual-like connections within one single residual block. The Res2Net represents multi-scale features at a granular level and increases the range of receptive fields for each network layer. The proposed Res2Net block can be plugged into the state-of-the-art backbone CNN models, e.g., ResNet, ResNeXt, and DLA. We evaluate the Res2Net block on all these models and demonstrate consistent performance gains over baseline models on widely-used datasets, e.g., CIFAR-100 and ImageNet. Further ablation studies and experimental results on representative computer vision tasks, i.e., object detection, class activation mapping, and salient object detection, further verify the superiority of the Res2Net over the state-of-the-art baseline methods. The source code and trained models are available on https://mmcheng.net/res2net/.Comment: 11 pages, 7 figure

SQ-SLAM: Monocular Semantic SLAM Based on Superquadric Object Representation

Object SLAM uses additional semantic information to detect and map objects in the scene, in order to improve the system's perception and map representation capabilities. Quadrics and cubes are often used to represent objects, but their single shape limits the accuracy of object map and thus affects the application of downstream tasks. In this paper, we introduce superquadrics (SQ) with shape parameters into SLAM for representing objects, and propose a separate parameter estimation method that can accurately estimate object pose and adapt to different shapes. Furthermore, we present a lightweight data association strategy for correctly associating semantic observations in multiple views with object landmarks. We implement a monocular semantic SLAM system with real-time performance and conduct comprehensive experiments on public datasets. The results show that our method is able to build accurate object map and has advantages in object representation. Code will be released upon acceptance.Comment: Submitted to ICRA 202

Using rewriting techniques to produce code generators and proving them correct

AbstractA major problem in deriving a compiler from a formal definition is the production of correct and efficient object code. In this context, we propose a solution to the problem of code-generator generation.Our approach is based on a target machine description where the basic concepts used (storage classes, access modes, access classes and instructions) are hierarchically described by tree patterns. These tree patterns are terms of an abstract data type. The program intermediate representation (input to the code generator) is a term of the same abstract data type.The code generation process is based on access modes and instruction template-driven rewritings. The result is that each program instruction is reduced to a sequence of elementary machine instructions, each of them representing an instance of an instruction template.The axioms of the abstract data type are used to prove that the rewritings preserve the semantics of the intermediate representation