Search Results for author: Aleš Leonardis

Found 23 papers, 9 papers with code

Wavelet-Based Dual-Branch Network for Image Demoiréing

no code implementations ECCV 2020 Lin Liu, Jianzhuang Liu, Shanxin Yuan, Gregory Slabaugh, Aleš Leonardis, Wengang Zhou, Qi Tian

When smartphone cameras are used to take photos of digital screens, usually moire patterns result, severely degrading photo quality.

Image Restoration Rain Removal

NCRF: Neural Contact Radiance Fields for Free-Viewpoint Rendering of Hand-Object Interaction

no code implementations8 Feb 2024 Zhongqun Zhang, Jifei Song, Eduardo Pérez-Pellitero, Yiren Zhou, Hyung Jin Chang, Aleš Leonardis

Despite remarkable progress that has been achieved in this field, existing methods still fail to synthesize the hand-object interaction photo-realistically, suffering from degraded rendering quality caused by the heavy mutual occlusions between the hand and the object, and inaccurate hand-object pose estimation.

hand-object pose Novel View Synthesis +3

Efficient View Synthesis and 3D-based Multi-Frame Denoising with Multiplane Feature Representations

1 code implementation CVPR 2023 Thomas Tanay, Aleš Leonardis, Matteo Maggioni

While current multi-frame restoration methods combine information from multiple input images using 2D alignment techniques, recent advances in novel view synthesis are paving the way for a new paradigm relying on volumetric scene representations.

Denoising Novel View Synthesis

Resource-Efficient RGBD Aerial Tracking

1 code implementation CVPR 2023 Jinyu Yang, Shang Gao, Zhe Li, Feng Zheng, Aleš Leonardis

However, current research on aerial perception has mainly focused on limited categories, such as pedestrian or vehicle, and most scenes are captured in urban environments from a birds-eye view.

Object Tracking

Learning Dual-Fused Modality-Aware Representations for RGBD Tracking

no code implementations6 Nov 2022 Shang Gao, Jinyu Yang, Zhe Li, Feng Zheng, Aleš Leonardis, Jingkuan Song

However, some existing RGBD trackers use the two modalities separately and thus some particularly useful shared information between them is ignored.

Object Tracking

Prompting for Multi-Modal Tracking

no code implementations29 Jul 2022 Jinyu Yang, Zhe Li, Feng Zheng, Aleš Leonardis, Jingkuan Song

Multi-modal tracking gains attention due to its ability to be more accurate and robust in complex scenarios compared to traditional RGB-based tracking.

Rgb-T Tracking

NTIRE 2022 Challenge on High Dynamic Range Imaging: Methods and Results

no code implementations25 May 2022 Eduardo Pérez-Pellitero, Sibi Catley-Chandar, Richard Shaw, Aleš Leonardis, Radu Timofte, Zexin Zhang, Cen Liu, Yunbo Peng, Yue Lin, Gaocheng Yu, Jin Zhang, Zhe Ma, Hongbin Wang, Xiangyu Chen, Xintao Wang, Haiwei Wu, Lin Liu, Chao Dong, Jiantao Zhou, Qingsen Yan, Song Zhang, Weiye Chen, Yuhang Liu, Zhen Zhang, Yanning Zhang, Javen Qinfeng Shi, Dong Gong, Dan Zhu, Mengdi Sun, Guannan Chen, Yang Hu, Haowei Li, Baozhu Zou, Zhen Liu, Wenjie Lin, Ting Jiang, Chengzhi Jiang, Xinpeng Li, Mingyan Han, Haoqiang Fan, Jian Sun, Shuaicheng Liu, Juan Marín-Vega, Michael Sloth, Peter Schneider-Kamp, Richard Röttger, Chunyang Li, Long Bao, Gang He, Ziyao Xu, Li Xu, Gen Zhan, Ming Sun, Xing Wen, Junlin Li, Shuang Feng, Fei Lei, Rui Liu, Junxiang Ruan, Tianhong Dai, Wei Li, Zhan Lu, Hengyan Liu, Peian Huang, Guangyu Ren, Yonglin Luo, Chang Liu, Qiang Tu, Fangya Li, Ruipeng Gang, Chenghua Li, Jinjing Li, Sai Ma, Chenming Liu, Yizhen Cao, Steven Tel, Barthelemy Heyrman, Dominique Ginhac, Chul Lee, Gahyeon Kim, Seonghyun Park, An Gia Vien, Truong Thanh Nhat Mai, Howoon Yoon, Tu Vo, Alexander Holston, Sheir Zaheer, Chan Y. Park

The challenge is composed of two tracks with an emphasis on fidelity and complexity constraints: In Track 1, participants are asked to optimize objective fidelity scores while imposing a low-complexity constraint (i. e. solutions can not exceed a given number of operations).

Image Restoration Vocal Bursts Intensity Prediction

RGBD Object Tracking: An In-depth Review

1 code implementation26 Mar 2022 Jinyu Yang, Zhe Li, Song Yan, Feng Zheng, Aleš Leonardis, Joni-Kristian Kämäräinen, Ling Shao

Particularly, we are the first to provide depth quality evaluation and analysis of tracking results in depth-friendly scenarios in RGBD tracking.

Object Object Tracking

CroMo: Cross-Modal Learning for Monocular Depth Estimation

no code implementations CVPR 2022 Yannick Verdié, Jifei Song, Barnabé Mas, Benjamin Busam, Aleš Leonardis, Steven McDonagh

Learning-based depth estimation has witnessed recent progress in multiple directions; from self-supervision using monocular video to supervised methods offering highest accuracy.

Monocular Depth Estimation

FlexHDR: Modelling Alignment and Exposure Uncertainties for Flexible HDR Imaging

no code implementations7 Jan 2022 Sibi Catley-Chandar, Thomas Tanay, Lucas Vandroux, Aleš Leonardis, Gregory Slabaugh, Eduardo Pérez-Pellitero

We introduce a strategy that learns to jointly align and assess the alignment and exposure reliability using an HDR-aware, uncertainty-driven attention map that robustly merges the frames into a single high quality HDR image.

Models Alignment

DepthTrack : Unveiling the Power of RGBD Tracking

1 code implementation31 Aug 2021 Song Yan, Jinyu Yang, Jani Käpylä, Feng Zheng, Aleš Leonardis, Joni-Kristian Kämäräinen

RGBD (RGB plus depth) object tracking is gaining momentum as RGBD sensors have become popular in many application fields such as robotics. However, the best RGBD trackers are extensions of the state-of-the-art deep RGB trackers.

Object Tracking

NTIRE 2021 Challenge on High Dynamic Range Imaging: Dataset, Methods and Results

1 code implementation2 Jun 2021 Eduardo Pérez-Pellitero, Sibi Catley-Chandar, Aleš Leonardis, Radu Timofte

This paper reviews the first challenge on high-dynamic range (HDR) imaging that was part of the New Trends in Image Restoration and Enhancement (NTIRE) workshop, held in conjunction with CVPR 2021.

HDR Reconstruction Image Restoration

A Survey on Neural Network Interpretability

no code implementations28 Dec 2020 Yu Zhang, Peter Tiňo, Aleš Leonardis, Ke Tang

Along with the great success of deep neural networks, there is also growing concern about their black-box nature.

Drug Discovery

Assessing Capsule Networks With Biased Data

no code implementations9 Apr 2019 Bruno Ferrarini, Shoaib Ehsan, Adrien Bartoli, Aleš Leonardis, Klaus D. McDonald-Maier

This paper aims to fill this gap and proposes two experimental scenarios to assess the tolerance to imbalanced training data and to determine the generalization performance of a model with unfamiliar affine transformations of the images.

Spatially-Adaptive Filter Units for Deep Neural Networks

2 code implementations CVPR 2018 Domen Tabernik, Matej Kristan, Aleš Leonardis

Classical deep convolutional networks increase receptive field size by either gradual resolution reduction or application of hand-crafted dilated convolutions to prevent increase in the number of parameters.

Image Classification Semantic Segmentation

Visual Stability Prediction and Its Application to Manipulation

no code implementations15 Sep 2016 Wenbin Li, Aleš Leonardis, Mario Fritz

We present a learning-based approach based on simulated data that predicts stability of towers comprised of wooden blocks under different conditions and quantities related to the potential fall of the towers.

Towards Deep Compositional Networks

no code implementations13 Sep 2016 Domen Tabernik, Matej Kristan, Jeremy L. Wyatt, Aleš Leonardis

We propose a novel analytic model of a basic unit in a layered hierarchical model with both explicit compositional structure and a well-defined discriminative cost function.

To Fall Or Not To Fall: A Visual Approach to Physical Stability Prediction

no code implementations31 Mar 2016 Wenbin Li, Seyedmajid Azimi, Aleš Leonardis, Mario Fritz

In this paper, we contrast a more traditional approach of taking a model-based route with explicit 3D representations and physical simulation by an end-to-end approach that directly predicts stability and related quantities from appearance.

Visual object tracking performance measures revisited

no code implementations20 Feb 2015 Luka Čehovin, Aleš Leonardis, Matej Kristan

The problem of visual tracking evaluation is sporting a large variety of performance measures, and largely suffers from lack of consensus about which measures should be used in experiments.

Object Visual Object Tracking +1

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