High-quality 3D object recognition is an important component of many vision and robotics systems. We tackle the object recognition problem using two data representations, to achieve leading results on the Princeton ModelNet challenge. The two representations: 1. Volumetric representation: the 3D object is discretized spatially as binary voxels - @math if the voxel is occupied and @math otherwise. 2. Pixel representation: the 3D object is represented as a set of projected 2D pixel images. Current leading submissions to the ModelNet Challenge use Convolutional Neural Networks (CNNs) on pixel representations. However, we diverge from this trend and additionally, use Volumetric CNNs to bridge the gap between the efficiency of the above two representations. We combine both representations and exploit them to learn new features, which yield a significantly better classifier than using either of the representations in isolation. To do this, we introduce new Volumetric CNN (V-CNN) architectures.
With recent improvements in the field of deep learning, Convolutional Neural Networks (CNN) have been widely and successfully used on 2D RGB images for a variety of tasks in computer vision, such as image classification @cite_29 @cite_22 , object detection, semantic segmentation @cite_19 @cite_4 and scene recognition @cite_13 .
@cite_4 @cite_22 @cite_29 @cite_19 @cite_13
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Deep convolutional neural networks (CNNs) have been immensely successful in many high-level computer vision tasks given large labeled datasets. However, for video semantic object segmentation, a domain where labels are scarce, effectively exploiting the representation power of CNN with limited training data remains a challenge. Simply borrowing the existing pretrained CNN image recognition model for video segmentation task can severely hurt performance. We propose a semi-supervised approach to adapting CNN image recognition model trained from labeled image data to the target domain exploiting both semantic evidence learned from CNN, and the intrinsic structures of video data. By explicitly modeling and compensating for the domain shift from the source domain to the target domain, this proposed approach underpins a robust semantic object segmentation method against the changes in appearance, shape and occlusion in natural videos. We present extensive experiments on challenging datasets that demonstrate the superior performance of our approach compared with the state-of-the-art methods. Deep convolutional neural networks (CNNs) have been immensely successful in many high-level computer vision tasks given large labelled datasets. However, for video semantic object segmentation, a domain where labels are scarce, effectively exploiting the representation power of CNN with limited training data remains a challenge. Simply borrowing the existing pre-trained CNN image recognition model for video segmentation task can severely hurt performance. We propose a semi-supervised approach to adapting CNN image recognition model trained from labelled image data to the target domain exploiting both semantic evidence learned from CNN, and the intrinsic structures of video data. By explicitly modelling and compensating for the domain shift from the source domain to the target domain, this proposed approach underpins a robust semantic object segmentation method against the changes in appearance, shape and occlusion in natural videos. We present extensive experiments on challenging datasets that demonstrate the superior performance of our approach compared with the state-of-the-art methods. Convolutional neural networks (CNN) have been widely used for visual recognition tasks including semantic segmentation of images. While the existing methods consider uniformly sampled single-or multi-scale patches from the neighborhood of each voxel, this approach might be sub-optimal as it captures and processes unnecessary details far away from the center of the patch. We instead propose to train CNNs with non-uniformly sampled patches that allow a wider extent for the sampled patches. This results in more captured contextual information, which is in particular of interest for biomedical image analysis, where the anatomical location of imaging features are often crucial. We evaluate and compare this strategy for white matter hyperintensity segmentation on a test set of 46 MRI scans. We show that the proposed method not only outperforms identical CNNs with uniform patches of the same size (0.780 Dice coefficient compared to 0.736), but also gets very close to the performance of an independent human expert (0.796 Dice coefficient). Three-dimensional convolution neural networks (3D CNN) have achieved great success in many computer vision applications, such as video analysis, medical image classification, and human action recognition. However, the efficiency of this model suffers from great computational intensity. In this work, we reduce the algorithmic complexity of 3D CNN to accelerate this model with Winograd’s minimal algorithm. We benchmark a net model on GPU platform, resulting in a speed-up by a factor of 1.2 ( ) compared with cuDNN, which is commonly used in many current machine learning frameworks.