Deep learning has improved many computer vision tasks by utilizing data-driven features instead of using hand-crafted features. However, geometric transformations of input images often degrade the performance of deep learning based methods. In particular, rotation-invariant features are important in computer vision tasks such as face detection, biological feature detection of microscopy images, or robot grasp detection since the input image can be fed into the network with any rotation angle. In this paper, we propose rotation ensemble module (REM) to efficiently train and utilize rotation-invariant features in a deep neural network for computer vision tasks. We evaluated our proposed REM with face detection tasks on FDDB dataset, robotic grasp detection tasks on Cornell dataset, and real robotic grasp tasks with several novel objects. REM based face detection deep neural networks yielded up to 50.8 accuracy in face detection task on FDDB dataset at false rate 20 with IOU 75 , which is about 10.7 higher than the baseline. Robotic grasp detection deep neural networks with our REM also yielded up to 97.6 accuracy in robotic grasp detection on Cornell dataset that is higher than current state-of-the-art performance. In robotic grasp task using a real 4-axis robotic arm with several novel objects, our REM based robotic grasp achieved up to 93.8 , which is significantly higher than the baseline robotic grasps (11.0-56.3 ).
. In general, max pooling layers in convolutional neural networks (CNN) are required to alleviate the issue of spatial variance in CNN. Assuming that spatial invariance is important for image classification, Jaderberg proposed spatial transformer network (STN), a method of image (or feature) transformation by learning (affine) transformation parameters so that it can help to improve the performance of inference operations of the following neural network layers @cite_2 . Lin proposed to use STN repeatedly with an inverse composite method by propagating warp parameters rather than image intensities (or features) unlike the original STN and it yielded improved performance over STN @cite_25 .
@cite_25 @cite_2
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In this work we describe a Convolutional Neural Network (CNN) to accurately predict the scene illumination. Taking image patches as input, the CNN works in the spatial domain without using hand-crafted features that are employed by most previous methods. The network consists of one convolutional layer with max pooling, one fully connected layer and three output nodes. Within the network structure, feature learning and regression are integrated into one optimization process, which leads to a more effective model for estimating scene illumination. This approach achieves state-of-the-art performance on a standard dataset of RAW images. Preliminary experiments on images with spatially varying illumination demonstrate the stability of the local illuminant estimation ability of our CNN. In this work we describe a Convolutional Neural Network (CNN) to accurately predict the scene illumination. Taking image patches as input, the CNN works in the spatial domain without using hand-crafted features that are employed by most previous methods. The network consists of one convolutional layer with max pooling, one fully connected layer and three output nodes. Within the network structure, feature learning and regression are integrated into one optimization process, which leads to a more effective model for estimating scene illumination. This approach achieves state-of-the-art performance on a standard dataset of RAW images. Preliminary experiments on images with spatially varying illumination demonstrate the stability of the local illuminant estimation ability of our CNN. Convolutional neural network (CNN) has achieved the state-of-the-art performance in many different visual tasks. Learned from a large-scale training data set, CNN features are much more discriminative and accurate than the handcrafted features. Moreover, CNN features are also transferable among different domains. On the other hand, traditional dictionary-based features (such as BoW and spatial pyramid matching) contain much more local discriminative and structural information, which is implicitly embedded in the images. To further improve the performance, in this paper, we propose to combine CNN with dictionary-based models for scene recognition and visual domain adaptation (DA). Specifically, based on the well-tuned CNN models (e.g., AlexNet and VGG Net), two dictionary-based representations are further constructed, namely, mid-level local representation (MLR) and convolutional Fisher vector (CFV) representation. In MLR, an efficient two-stage clustering method, i.e., weighted spatial and feature space spectral clustering on the parts of a single image followed by clustering all representative parts of all images, is used to generate a class-mixture or a class-specific part dictionary. After that, the part dictionary is used to operate with the multiscale image inputs for generating mid-level representation. In CFV, a multiscale and scale-proportional Gaussian mixture model training strategy is utilized to generate Fisher vectors based on the last convolutional layer of CNN. By integrating the complementary information of MLR, CFV, and the CNN features of the fully connected layer, the state-of-the-art performance can be achieved on scene recognition and DA problems. An interested finding is that our proposed hybrid representation (from VGG net trained on ImageNet) is also complementary to GoogLeNet and or VGG-11 (trained on Place205) greatly. Despite that convolutional neural networks (CNN) have recently demonstrated high-quality reconstruction for single-image super-resolution (SR), recovering natural and realistic texture remains a challenging problem. In this paper, we show that it is possible to recover textures faithful to semantic classes. In particular, we only need to modulate features of a few intermediate layers in a single network conditioned on semantic segmentation probability maps. This is made possible through a novel Spatial Feature Transform (SFT) layer that generates affine transformation parameters for spatial-wise feature modulation. SFT layers can be trained end-to-end together with the SR network using the same loss function. During testing, it accepts an input image of arbitrary size and generates a high-resolution image with just a single forward pass conditioned on the categorical priors. Our final results show that an SR network equipped with SFT can generate more realistic and visually pleasing textures in comparison to state-of-the-art SRGAN [27] and EnhanceNet [38].