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Nov 14, 2019 · Resize an Image by Setting an Exact Height and Width . Resize an object based on an exact size if you need to make two or more images the same size or if images must be a certain size to fit a template or other requirement.

We propose a self-supervised approach for learning representations of objects from monocular videos and demonstrate it is particularly useful in situated settings such as robotics. The main contributions of this paper are:

An interesting and perhaps surprising finding of this approach is that given a limited set of objects, object correspondences will naturally emerge when using contrastive learning without requiring explicit positive pairs. Videos illustrating online object adaptation and robotic pointing are available as supplementary material.

One of the biggest challenges in real world robotics is robustness and adaptability to new situations. A robot deployed in the real world is likely to encounter a number of objects it has never seen before. Even if it can identify the class of an object, it may be useful to recognize a particular instance of it. Relying on human supervision in this context is unrealistic. Instead if a robot can self-supervise its understanding of objects, it can adapt to new situations when using online learning. Online self-supervision is key to robustness and adaptability and arguably a prerequisite to real-world deployment. Moreover, removing human supervision has the potential to enable learning richer and less biased continuous representations than those obtained by supervised training and a limited set of discrete labels. Unbiased representations can prove useful in unknown future environments different from the ones seen during supervision, a typical challenge for robotics. Furthermore, the ability to autonomously train to recognize and differentiate previously unseen objects as well as to infer general properties and attributes is an important skill for robotic agents.

In this work we focus on situated settings (i.e. an agent is embedded in an environment), which allows us to use temporal continuity as the basis for self-supervising correspondences between different views of objects. We present a self-supervised method that learns representations to disentangle perceptual and semantic object attributes such as class, function, and color. We automatically acquire training data by capturing videos with a real robot; a robot base moves around a table to capture objects in various arrangements. Assuming a pre-existing objectness detector, we extract objects from random frames of a scene containing the same objects, and let a metric learning system decide how to assign positive and negative pairs of embeddings. Representations that generalize across objects naturally emerge despite not being given groundtruth matches. Unlike previous methods, we abstain from employing additional self-supervisory training signals such as depth or those used for tracking. The only input to the system are monocular videos. This simplifies data collection and allows our embedding to integrate into existing end-to-end learning pipelines. We demonstrate that a trained Object-Contrastive Network (OCN) embedding allows us to reliably identify object instances based on their visual features such as color and shape. Moreover, we show that objects are also organized along their semantic or functional properties. For example, a cup might not only be associated with other cups, but also with other containers like bowls or vases.

Figure 1 shows the effectiveness of online self-supervision: by training on randomly selected frames of a continuous video sequence (top) OCN can adapt to the present objects and thereby lower the object identification error. While the supervised baseline remains at a constant high error rate (52.4%), OCN converges to a 2.2% error. The graph (bottom) shows the object identification error obtained by training on progressively longer sub-sequences of a 200 seconds video.

The fact that this works at all despite not using any labels might be surprising. One of the main findings of this paper is that given a limited set of objects, object correspondences will naturally emerge when using metric learning. One advantage of self-supervising object representation is that these continuous representations are not biased by or limited to a discrete set of labels determined by human annotators. We show these embeddings discover and disentangle object attributes and generalize to previously unseen environments.

We propose a self-supervised approach to learn object representations for the following reasons: (1) make data collection simple and scalable, (2) increase autonomy in robotics by continuously learning about new objects without assistance, (3) discover continuous representations that are richer and more nuanced than the discrete set of attributes that humans might provide as supervision which may not match future and new environments. All these objectives require a method that can learn about objects and differentiate them without supervision. To bootstrap our learning signal we leverage two assumptions: (1) we are provided with a general objectness model so that we can attend to individual objects in a scene, (2) during an observation sequence the same objects will be present in most frames (this can later be relaxed by using an approximate estimation of ego-motion). Given a video sequence around a scene containing multiple objects, we randomly select two frames II I and I^\hat{I} ​ I ​ ^ ​ ​ in the sequence and detect the objects present in each image. Let us assume NN N and MM M objects are detected in image II I and I^\hat{I} ​ I ​ ^ ​ ​ , respectively. Each of the nn n -th and mm m -th cropped object images are embedded in a low dimensional space, organized by a metric learning objective. Unlike traditional methods which rely on human-provided similarity labels to drive metric learning, we use a self-supervised approach to mine synthetic similarity labels.

Objectness Detection: To detect objects, we use Faster-RCNN trained on the COCO object detection dataset . Faster-RCNN detects objects in two stages: first generate class-agnostic bounding box proposals of all objects present in an image (Figure 1), second associate detected objects with class labels. We use OCN to discover object attributes, and only rely on the first objectness stage of Faster-R-CNN to detect object candidates.

Metric Loss for Object Disentanglement: We denote a cropped object image by xXx \in \mathcal{X} x X and compute its embedding based on a convolutional neural network f(x):X Kf(x): \mathcal{X} \rightarrow K f ( x ) : X K . Note that for simplicity we may omit xx x from f(x)f(x) f ( x ) while ff f inherits all superscripts and subscripts. Let us consider two pairs of images II I and I^\hat{I} ​ I ​ ^ ​ ​ that are taken at random from the same contiguous observation sequence. Let us also assume there are nn n and mm m objects detected in II I and I^\hat{I} ​ I ​ ^ ​ ​ respectively. We denote the nn n -th and mm m -th objects in the images II I and I^\hat{I} ​ I ​ ^ ​ ​ as xnIx_n^{I} x ​ n ​ I ​ ​ and xmI^x_m^{\hat{I}} x ​ m ​ ​ I ​ ^ ​ ​ ​ ​ , respectively.

The purpose of Simbad is to provide information on astronomical objects of interest which have been studied in scientific articles.

It provides the bibliography, as well as available basic information such as the nature of the object, its coordinates, magnitudes, proper motions and parallax, velocity/redshift, size, spectral or morphological type, and the multitude of names (identifiers) given in the literature. The CDS team also performs cross-identifications based on the compatibility of several parameters, in the limit of a reasonably good astrometry.

Simbad is not a catalogue, and should not be used as a catalogue. The CDS also provides the VizieR database which contains published lists of objects, as well as most very large surveys. The idea now is to use both Simbad and VizieR as completary research tools.

Content The SIMBAD astronomical database provides basic data, cross-identifications, bibliography and measurements for astronomical objects outside the solar system. SIMBAD can be queried by object name, coordinates and various criteria. Lists of objects and scripts can be submitted. Links to some other on-line services are also provided. Basic search identifier, coordinates (radius 10 arcmin), or bibcode help Install the Simbad basic search in your tool bar

Acknowledgment If the Simbad database was helpful for your research work, the following acknowledgment would be appreciated: This research has made use of the SIMBAD database, operated at CDS, Strasbourg, France 2000,AAS,143,9 , , Wenger et al. Statistics Simbad contains on 11,521,237 objects 36,601,686 identifiers 379,995 bibliographic references 22,732,241 citations of objects in papers

A Koa application is an object containing an array of middleware functions which are composed and executed in a stack-like manner upon request. Koa is similar to many other middleware systems that you may have encountered such as Rubys Rack, Connect, and so on - however a key design decision was made to provide high level at the otherwise low-level middleware layer. This improves interoperability, robustness, and makes writing middleware much more enjoyable.

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