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Improving Point Cloud Semantic Segmentation by Learning 3D Object Detection
(2021)2021 IEEE Winter Conference on Applications of Computer Vision (WACV)Point cloud semantic segmentation plays an essential role in autonomous driving, providing vital information about drivable surfaces and nearby objects that can aid higher level tasks such as path planning and collision avoidance. While current 3D semantic segmentation networks focus on convolutional architectures that perform great for well represented classes, they show a significant drop in performance for underrepresented classes that ...Conference Paper -
Zero-Pair Image to Image Translation using Domain Conditional Normalization
(2021)2021 IEEE Winter Conference on Applications of Computer Vision (WACV)In this paper, we propose an approach based on domain conditional normalization (DCN) for zero-pair image-to-image translation, i.e., translating between two domains which have no paired training data available but each have paired training data with a third domain. We employ a single generator which has an encoder-decoder structure and analyze different implementations of domain conditional normalization to obtain the desired target ...Conference Paper -
Neural Architecture Search of SPD Manifold Networks
(2021)Proceedings of the Thirtieth International Joint Conference on Artificial IntelligenceConference Paper -
Group sparsity: The Hinge Between Filter Pruning and Decomposition for Network Compression
(2020)2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)In this paper, we analyze two popular network compression techniques, i.e. filter pruning and low-rank decomposition, in a unified sense. By simply changing the way the sparsity regularization is enforced, filter pruning and low-rank decomposition can be derived accordingly. This provides another flexible choice for network compression because the techniques complement each other. For example, in popular network architectures with shortcut ...Conference Paper -
Domain Agnostic Feature Learning for Image and Video Based Face Anti-spoofing
(2020)2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)Nowadays, the increasingly growing number of mobile and computing devices has led to a demand for safer user authentication systems. Face anti-spoofing is a measure towards this direction for biometric user authentication, and in particular face recognition, that tries to prevent spoof attacks. The state-of-the-art anti-spoofing techniques leverage the ability of deep neural networks to learn discriminative features, based on cues from ...Conference Paper -
Three Ways to Improve Semantic Segmentation with Self-Supervised Depth Estimation
(2021)2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)Training deep networks for semantic segmentation requires large amounts of labeled training data, which presents a major challenge in practice, as labeling segmentation masks is a highly labor-intensive process. To address this issue, we present a framework for semi-supervised semantic segmentation, which is enhanced by self-supervised monocular depth estimation from unlabeled image sequences. In particular, we propose three key contributions: ...Conference Paper -
Flow-based Kernel Prior with Application to Blind Super-Resolution
(2021)2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)Kernel estimation is generally one of the key problems for blind image super-resolution (SR). Recently, Double-DIP proposes to model the kernel via a network architecture prior, while KernelGAN employs the deep linear network and several regularization losses to constrain the kernel space. However, they fail to fully exploit the general SR kernel assumption that anisotropic Gaussian kernels are sufficient for image SR. To address this ...Conference Paper -
Viewpoint consistent texture synthesis
(2004)Proceedings. 2nd International Symposium on 3D Data Processing, Visualization, and TransmissionThe purpose of this work is to synthesize textures of rough, real world surfaces under freely chosen viewing and illumination directions. Moreover, such textures are produced for continuously changing directions in such a way that the different textures are mutually consistent, i.e. emulate the same piece of surface. This is necessary for 3D animation. It is assumed that the mesostructure (small-scale) geometry of a surface is not known, ...Conference Paper -
Real-Time Detection of Unusual Regions in Image Streams
(2010)Proceedings of the ACM Multimedia 2010 International ConferenceConference Paper -
Unsupervised Deep Single‐Image Intrinsic Decomposition using Illumination‐Varying Image Sequences
(2018)Computer Graphics ForumMachine learning based Single Image Intrinsic Decomposition (SIID) methods decompose a captured scene into its albedo and shading images by using the knowledge of a large set of known and realistic ground truth decompositions. Collecting and annotating such a dataset is an approach that cannot scale to sufficient variety and realism. We free ourselves from this limitation by training on unannotated images. Our method leverages the observation ...Conference Paper