Object Localization is the task of locating an instance of a particular object category in an image, typically by specifying a tightly cropped bounding box centered on the instance. An object proposal specifies a candidate bounding box, and an object proposal is said to be a correct localization if it sufficiently overlaps a human-labeled “ground-truth” bounding box for the given object. In the literature, the “Object Localization” task is to locate one instance of an object category, whereas “object detection” focuses on locating all instances of a category in a given image.
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Unfortunately, the network activates only the features that discriminate the object and does not activate the whole object.
At the heart of this progress is convolutional neural networks (CNNs) that are capable of learning representations or features given a set of data.
The detector predicts the object location defined by a set of coefficients describing a geometric shape (i. e. ellipse or rectangle), which is geometrically constrained by the mask produced by the generator.
Most existing works attempt post-hoc interpretation on a pre-trained model, while neglecting to reduce the entanglement underlying the model.
Monocular multi-object detection and localization in 3D space has been proven to be a challenging task.
The proposed deep learning method consists of a two-stage object detection network to produce region of interest (RoI) features and a building boundary extraction network using graph models to learn geometric information of the polygon shapes.
To fulfill the direct evaluation, we annotate pixel-level object masks on the ILSVRC validation set.
In this paper, we argue that WSOL task is ill-posed with only image-level labels, and propose a new evaluation protocol where full supervision is limited to only a small held-out set not overlapping with the test set.
We propose a novel method that tracks fast moving objects, mainly non-uniform spherical, in full 6 degrees of freedom, estimating simultaneously their 3D motion trajectory, 3D pose and object appearance changes with a time step that is a fraction of the video frame exposure time.