We present a new approach for sampling conditional measures that enables uncertainty quantification in supervised learning tasks. We construct a mapping that transforms a reference measure to the probability measure of the output conditioned on new inputs. The mapping is trained via a modification of generative adversarial networks (GANs), called monotone GANs, that imposes monotonicity constraints and a block triangular structure. We present theoretical results, in an idealized setting, that support our proposed method as well as numerical experiments demonstrating the ability of our method to sample the correct conditional measures in applications ranging from inverse problems to image in-painting.
Depth completion aims to predict a dense depth map from a sparse depth input. The acquisition of dense ground truth annotations for depth completion settings can be difficult and, at the same time, a significant domain gap between real LiDAR measurements and synthetic data has prevented from successful training of models in virtual settings. We propose a domain adaptation approach for sparse-to-dense depth completion that is trained from synthetic data, without annotations in the real domain or additional sensors. Our approach simulates the real sensor noise in an RGB+LiDAR set-up, and consists of three modules: simulating the real LiDAR input in the synthetic domain via projections, filtering the real noisy LiDAR for supervision and adapting the synthetic RGB image using a CycleGAN approach. We extensively evaluate these modules against the state-of-the-art in the KITTI depth completion benchmark, showing significant improvements.
Image representation and classification are two fundamental tasks towards multimedia content retrieval and understanding. The idea that shape and texture information (e.g. edge or orientation) are the key features for visual representation is ingrained and dominated in current multimedia and computer vision communities. A number of low-level features have been proposed by computing local gradients (e.g. SIFT, LBP and HOG), and have achieved great successes on numerous multimedia applications. In this paper, we present a simple yet efficient local descriptor for image classification, referred as Local Color Contrastive Descriptor (LCCD), by leveraging the neural mechanisms of color contrast. The idea originates from the observation in neural science that color and shape information are linked inextricably in visual cortical processing. The color contrast yields key information for visual color perception and provides strong linkage between color and shape. We propose a novel contrastive mechanism to compute the color contrast in both spatial location and multiple channels. The color contrast is computed by measuring \emph{f}-divergence between the color distributions of two regions. Our descriptor enriches local image representation with both color and contrast information. We verified experimentally that it can compensate strongly for the shape based descriptor (e.g. SIFT), while keeping computationally simple. Extensive experimental results on image classification show that our descriptor improves the performance of SIFT substantially by combinations, and achieves the state-of-the-art performance on three challenging benchmark datasets. It improves recent Deep Learning model (DeCAF) [1] largely from the accuracy of 40.94% to 49.68% in the large scale SUN397 database. Codes for the LCCD will be available.
Recently, impressive denoising results have been achieved by Bayesian approaches which assume Gaussian models for the image patches. This improvement in performance can be attributed to the use of per-patch models. Unfortunately such an approach is particularly unstable for most inverse problems beyond denoising. In this work, we propose the use of a hyperprior to model image patches, in order to stabilize the estimation procedure. There are two main advantages to the proposed restoration scheme: Firstly it is adapted to diagonal degradation matrices, and in particular to missing data problems (e.g. inpainting of missing pixels or zooming). Secondly it can deal with signal dependent noise models, particularly suited to digital cameras. As such, the scheme is especially adapted to computational photography. In order to illustrate this point, we provide an application to high dynamic range imaging from a single image taken with a modified sensor, which shows the effectiveness of the proposed scheme.
Effective defense of deep neural networks against adversarial attacks remains a challenging problem, especially under powerful white-box attacks. In this paper, we develop a new method called ensemble generative cleaning with feedback loops (EGC-FL) for effective defense of deep neural networks. The proposed EGC-FL method is based on two central ideas. First, we introduce a transformed deadzone layer into the defense network, which consists of an orthonormal transform and a deadzone-based activation function, to destroy the sophisticated noise pattern of adversarial attacks. Second, by constructing a generative cleaning network with a feedback loop, we are able to generate an ensemble of diverse estimations of the original clean image. We then learn a network to fuse this set of diverse estimations together to restore the original image. Our extensive experimental results demonstrate that our approach improves the state-of-art by large margins in both white-box and black-box attacks. It significantly improves the classification accuracy for white-box PGD attacks upon the second best method by more than 29% on the SVHN dataset and more than 39% on the challenging CIFAR-10 dataset.
Following the presentation and proof of the hypothesis that image features are particularly perceived at points where the Fourier components are maximally in phase, the concept of phase congruency (PC) is introduced. Subsequently, a two-dimensional multi-scale phase congruency (2D-MSPC) is developed, which has been an important tool for detecting and evaluation of image features. However, the 2D-MSPC requires many parameters to be appropriately tuned for optimal image features detection. In this paper, we defined a criterion for parameter optimization of the 2D-MSPC, which is a function of its maximum and minimum moments. We formulated the problem in various optimal and suboptimal frameworks, and discussed the conditions and features of the suboptimal solutions. The effectiveness of the proposed method was verified through several examples, ranging from natural objects to medical images from patients with a neurological disease, multiple sclerosis.
Noise injection has been proved to be one of the key technique advances in generating high-fidelity images. Despite its successful usage in GANs, the mechanism of its validity is still unclear. In this paper, we propose a geometric framework to theoretically analyze the role of noise injection in GANs. Based on Riemannian geometry, we successfully model the noise injection framework as fuzzy equivalence on the geodesic normal coordinates. Guided by our theories, we find that the existing method is incomplete and a new strategy for noise injection is devised. Experiments on image generation and GAN inversion demonstrate the superiority of our method.
Do we need to have more expensive remote sensing satellites when we could use single image super-resolution (SISR) to get the spatial resolution that we want? By using a Super Resolution Generative Adversarial Network, (SRGAN) we can get higher resolution images. Previous work by Shermeyer et al. [1] have used SISR as a preprocessing step describe an increase in mAP of 10-36 % in object detection for native 30cm to 15cm satellite imagery. This suggests a possible improvement to automated target recognition in image classification and object detection. The SRGAN takes a low-resolution image and maps it to a high-resolution image creating the super resolution product. We train 5 SRGANs on different land-use classes (e.g. agriculture, cities) and find the qualitative and quantitative differences in SISR, binary classification, and object detection performance.
This paper introduces an improved reranking method for the Bag-of-Words (BoW) based image search. Built on [1], a directed image graph robust to outlier distraction is proposed. In our approach, the relevance among images is encoded in the image graph, based on which the initial rank list is refined. Moreover, we show that the rank-level feature fusion can be adopted in this reranking method as well. Taking advantage of the complementary nature of various features, the reranking performance is further enhanced. Particularly, we exploit the reranking method combining the BoW and color information. Experiments on two benchmark datasets demonstrate that ourmethod yields significant improvements and the reranking results are competitive to the state-of-the-art methods.
We consider the problem of learning to drive low-cost small scale cars using reinforcement learning. It is challenging to handle the long-tailed distributions of events in the real-world with handcrafted logical rules and reinforcement learning could be a potentially more scalable solution to deal with them. We adopt an existing platform called Donkey car for low-cost repeatable and reproducible research in autonomous driving. We consider the task of learning to drive around a track, given only monocular image observations from an on-board camera. We demonstrate that the soft actor-critic algorithm combined with state representation learning using a variational autoencoder can learn to drive around randomly generated tracks on the Donkey car simulator and a real-world track using the Donkey car platform. Our agent can learn from scratch using sparse and noisy rewards within just 10 minutes of driving experience.