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Real-time 3D Traffic Cone Detection for Autonomous Driving
(2019)2019 IEEE Intelligent Vehicles Symposium (IV)Conference Paper -
Is Image Super-resolution Helpful for Other Vision Tasks?
(2016)2016 IEEE Winter Conference on Applications of Computer Vision (WACV)Despite the great advances made in the field of image super-resolution (ISR) during the last years, the performance has merely been evaluated perceptually. Thus, it is still unclear whether ISR is helpful for other vision tasks. In this paper, we present the first comprehensive study and analysis of the usefulness of ISR for other vision applications. In particular, six ISR methods are evaluated on four popular vision tasks, namely edge ...Conference Paper -
Learning Accurate and Human-Like Driving using Semantic Maps and Attention
(2020)2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)This paper investigates how end-to-end driving models can be improved to drive more accurately and human-like. To tackle the first issue we exploit semantic and visual maps from HERE Technologies and augment the existing Drive360 dataset with such. The maps are used in an attention mechanism that promotes segmentation confidence masks, thus focusing the network on semantic classes in the image that are important for the current driving ...Conference Paper -
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 -
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 -
Pix2NeRF: Unsupervised Conditional π-GAN for Single Image to Neural Radiance Fields Translation
(2022)2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)We propose a pipeline to generate Neural Radiance Fields (NeRF) of an object or a scene of a specific class, conditioned on a single input image. This is a challenging task, as training NeRF requires multiple views of the same scene, coupled with corresponding poses, which are hard to obtain. Our method is based on pi-GAN, a generative model for unconditional 3D-aware image synthesis, which maps random latent codes to radiance fields of ...Conference Paper -
Spectral Tensor Train Parameterization of Deep Learning Layers
(2021)Proceedings of Machine Learning Research ~ Proceedings of the 24th International Conference on Artificial Intelligence and Statistics (AISTATS 2021)We study low-rank parameterizations of weight matrices with embedded spectral properties in the Deep Learning context. The low-rank property leads to parameter efficiency and permits taking computational shortcuts when computing mappings. Spectral properties are often subject to constraints in optimization problems, leading to better models and stability of optimization. We start by looking at the compact SVD parameterization of weight ...Conference Paper -
Leveraging single for multi-target tracking using a novel trajectory overlap affinity measure
(2016)2016 IEEE Winter Conference on Applications of Computer Vision (WACV)Multi-target tracking (MTT) is the task of localizing objects of interest in a video and associating them through time. Accurate affinity measures between object detections is crucial for MTT. Previous methods use simple affinity measures, based on heuristics, that are unable to track through occlusions and missing detections. To address this problem, this paper proposes a novel affinity measure by leveraging the power of single-target ...Conference Paper -
T-Basis: a Compact Representation for Neural Networks
(2020)Proceedings of Machine Learning Research ~ Proceedings of the 37th International Conference on Machine LearningWe introduce T-Basis, a novel concept for a compact representation of a set of tensors, each of an arbitrary shape, which is often seen in Neural Networks. Each of the tensors in the set is modeled using Tensor Rings, though the concept applies to other Tensor Networks. Owing its name to the T-shape of nodes in diagram notation of Tensor Rings, T-Basis is simply a list of equally shaped three-dimensional tensors, used to represent Tensor ...Conference Paper -
Fast algorithms for linear and kernel SVM+
(2016)Proceedings of the 29th IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2016)Conference Paper