Learning Unsupervised Object Localization for 6-DoF Scene Labeling


Learning Unsupervised Object Localization for 6-DoF Scene Labeling – The success of recent deep learning-based vision systems for object localization has led to the development of large-scale object localization systems. These systems are challenging in that the tasks are hard for humans to do and humans usually cannot track objects at all and most of objects have no geometric appearance (such as their position). Thus, this work proposes a novel deep learning-based learning system to classify objects at multiple levels of the scene. This system aims at solving multi-dimensional object localization tasks such as object detection, object appearance, and object pose, using object detection and pose matching as two crucial components. The proposed system was trained using 3D-LSTM and trained using a convolutional neural network (CNN), aiming at identifying objects on the first level and the object pose over multiple levels. The system evaluated its effectiveness on object detection task including detection of the objects at the second, third and fourth levels (from the first to the second). Results show that our algorithm significantly improved the overall performance on the problem of object detection and pose matching.

In this paper we present a novel deep learning framework to encode the input temporal data in a recurrent network. The objective is to extract multiple instances of the same object and place the object into an image. The model aims at inferring object poses from temporal images. The object and its pose are represented by a spatial grid of 3D points. The network is trained by solving multi-task multi-view retrieval task by combining multiple tasks, one of which is object pose extraction with the other one. The learned object poses have been learned with the same spatial grid that was used to represent the real world. These multi-task multi-view object pose inference is made by a supervised classification task. The objective is to extract multiple instances of the same object. We propose a method to encode the temporal data into a spatial grid. We apply the learned object poses to 2D image representation, and show that it outperforms state-of-the-art multi-task-based retrieval methods when compared to the other state-of-the-art methods. We demonstrate the effectiveness of our model by training on large datasets from the Google+ community.

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Learning Unsupervised Object Localization for 6-DoF Scene Labeling

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  • Towards Optimal Vehicle Detection and Steering

    Multi-Context Attention for Spatial-Temporal ReasoningIn this paper we present a novel deep learning framework to encode the input temporal data in a recurrent network. The objective is to extract multiple instances of the same object and place the object into an image. The model aims at inferring object poses from temporal images. The object and its pose are represented by a spatial grid of 3D points. The network is trained by solving multi-task multi-view retrieval task by combining multiple tasks, one of which is object pose extraction with the other one. The learned object poses have been learned with the same spatial grid that was used to represent the real world. These multi-task multi-view object pose inference is made by a supervised classification task. The objective is to extract multiple instances of the same object. We propose a method to encode the temporal data into a spatial grid. We apply the learned object poses to 2D image representation, and show that it outperforms state-of-the-art multi-task-based retrieval methods when compared to the other state-of-the-art methods. We demonstrate the effectiveness of our model by training on large datasets from the Google+ community.


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