no code implementations • 25 Apr 2024 • Ye Mao, Junpeng Jing, Krystian Mikolajczyk
However, the limited color and texture variations in CAD images can compromise the alignment robustness.
no code implementations • 23 Apr 2024 • Michal Nazarczuk, Jan Kristof Behrens, Karla Stepanova, Matej Hoffmann, Krystian Mikolajczyk
Embodied reasoning systems integrate robotic hardware and cognitive processes to perform complex tasks typically in response to a natural language query about a specific physical environment.
no code implementations • 10 Apr 2024 • Andrej Kruzliak, Jiri Hartvich, Shubhan P. Patni, Lukas Rustler, Jan Kristof Behrens, Fares J. Abu-Dakka, Krystian Mikolajczyk, Ville Kyrki, Matej Hoffmann
The robot pipeline integrates with a logging module and an online database of objects, containing over 24, 000 measurements of 63 objects with different grippers.
no code implementations • 22 Mar 2024 • Dylan Auty, Krystian Mikolajczyk
In this work, we demonstrate the use of natural language as a source of an explicit prior about the structure of the world.
no code implementations • 21 Mar 2024 • Dylan Auty, Roy Miles, Benedikt Kolbeinsson, Krystian Mikolajczyk
In this setting, cross-task distillation can be used, enabling the use of any teacher model trained on a different task.
no code implementations • 16 Mar 2024 • Junpeng Jing, Ye Mao, Krystian Mikolajczyk
Towards this challenge, we develop a bidirectional alignment mechanism for adjacent frames as a fundamental operation.
no code implementations • 19 Dec 2023 • Benedikt Kolbeinsson, Krystian Mikolajczyk
Accurate depth and semantic segmentation are crucial for various computer vision tasks.
no code implementations • 29 Nov 2023 • Mikolaj Jankowski, Deniz Gunduz, Krystian Mikolajczyk
In this work, we study early exiting in the context of collaborative inference, which allows obtaining inference results at the edge device for certain samples, without the need to transmit the partially processed data to the edge server at all, leading to further communication savings.
no code implementations • 17 Apr 2023 • Haotian Wu, Nitish Mital, Krystian Mikolajczyk, Deniz Gündüz
We study the collaborative image retrieval problem at the wireless edge, where multiple edge devices capture images of the same object, which are then used jointly to retrieve similar images at the edge server over a shared multiple access channel.
4 code implementations • 20 Mar 2023 • Roy Miles, Krystian Mikolajczyk
We then show that the normalisation of representations is tightly coupled with the training dynamics of this projector, which can have a large impact on the students performance.
Ranked #4 on Knowledge Distillation on ImageNet
1 code implementation • 1 Dec 2022 • Benedikt Kolbeinsson, Krystian Mikolajczyk
Semantic segmentation from aerial views is a crucial task for autonomous drones, as they rely on precise and accurate segmentation to navigate safely and efficiently.
1 code implementation • 30 Nov 2022 • Dylan Auty, Krystian Mikolajczyk
While monocular depth estimation (MDE) is an important problem in computer vision, it is difficult due to the ambiguity that results from the compression of a 3D scene into only 2 dimensions.
no code implementations • 1 Jun 2022 • Michal Nazarczuk, Tony Ng, Krystian Mikolajczyk
Humans exhibit incredibly high levels of multi-modal understanding - combining visual cues with read, or heard knowledge comes easy to us and allows for very accurate interaction with the surrounding environment.
1 code implementation • 21 Apr 2022 • Dylan Auty, Krystian Mikolajczyk
Part of the MDE task is, therefore, to learn which visual cues in the image can be used for depth estimation, and how.
no code implementations • CVPR 2022 • Tony Ng, Hyo Jin Kim, Vincent Lee, Daniel DeTone, Tsun-Yi Yang, Tianwei Shen, Eddy Ilg, Vassileios Balntas, Krystian Mikolajczyk, Chris Sweeney
We let a feature encoding network and image reconstruction network compete with each other, such that the feature encoder tries to impede the image reconstruction with its generated descriptors, while the reconstructor tries to recover the input image from the descriptors.
1 code implementation • CVPR 2022 • Axel Barroso-Laguna, Yurun Tian, Krystian Mikolajczyk
We formulate the scale estimation problem as a prediction of a probability distribution over scale factors.
1 code implementation • 1 Dec 2021 • Roy Miles, Adrian Lopez Rodriguez, Krystian Mikolajczyk
Despite the empirical success of knowledge distillation, current state-of-the-art methods are computationally expensive to train, which makes them difficult to adopt in practice.
Classification with Binary Weight Network Knowledge Distillation +1
no code implementations • 25 Oct 2021 • Roy Miles, Krystian Mikolajczyk
We present an efficient alternative to the convolutional layer using cheap spatial transformations.
no code implementations • 6 Oct 2021 • Ruijie Ren, Mohit Gurnani Rajesh, Jordi Sanchez-Riera, Fan Zhang, Yurun Tian, Antonio Agudo, Yiannis Demiris, Krystian Mikolajczyk, Francesc Moreno-Noguer
We show that training our network solely with synthetic data and the proposed DA yields results competitive with models trained on real data.
no code implementations • 16 Aug 2021 • Tony Ng, Adrian Lopez-Rodriguez, Vassileios Balntas, Krystian Mikolajczyk
In this paper, we address the problem of camera pose estimation in outdoor and indoor scenarios.
no code implementations • 24 May 2021 • Mikolaj Jankowski, Deniz Gunduz, Krystian Mikolajczyk
We further improve the performance of AirNet by pruning the network below the available bandwidth, and expanding it for improved robustness.
no code implementations • 3 Sep 2020 • Adrian Lopez-Rodriguez, Krystian Mikolajczyk
Instead, in this paper, we propose a domain adaptation approach to train a monocular depth estimation model using a fully-annotated source dataset and a non-annotated target dataset.
no code implementations • 16 Aug 2020 • Roy Miles, Krystian Mikolajczyk
In this paper, we propose an approach for filter-level pruning with hierarchical knowledge distillation based on the teacher, teaching-assistant, and student framework.
no code implementations • 3 Aug 2020 • Adrian Lopez-Rodriguez, Benjamin Busam, Krystian Mikolajczyk
Depth completion aims to predict a dense depth map from a sparse depth input.
no code implementations • 21 Jul 2020 • Mikolaj Jankowski, Deniz Gunduz, Krystian Mikolajczyk
We propose two alternative schemes based on digital and analog communications, respectively.
1 code implementation • NeurIPS 2020 • Yurun Tian, Axel Barroso-Laguna, Tony Ng, Vassileios Balntas, Krystian Mikolajczyk
Recent works show that local descriptor learning benefits from the use of L2 normalisation, however, an in-depth analysis of this effect lacks in the literature.
no code implementations • 27 May 2020 • Yurun Tian, Vassileios Balntas, Tony Ng, Axel Barroso-Laguna, Yiannis Demiris, Krystian Mikolajczyk
In this paper, we present a novel approach that exploits the information within the descriptor space to propose keypoint locations.
1 code implementation • 12 May 2020 • Axel Barroso-Laguna, Yannick Verdie, Benjamin Busam, Krystian Mikolajczyk
Local feature extraction remains an active research area due to the advances in fields such as SLAM, 3D reconstructions, or AR applications.
no code implementations • 6 Apr 2020 • Michal Nazarczuk, Krystian Mikolajczyk
In this paper we present an approach and a benchmark for visual reasoning in robotics applications, in particular small object grasping and manipulation.
no code implementations • 9 Mar 2020 • Nima Mohammadi Meshky, Sara Iodice, Krystian Mikolajczyk
However, in poorly-lit environments CCTV cameras switch to infrared imaging, hence developing a system which can correctly perform matching between infrared and colour images is a necessity.
no code implementations • 4 Mar 2020 • Mikolaj Jankowski, Deniz Gunduz, Krystian Mikolajczyk
We propose a joint feature compression and transmission scheme for efficient inference at the wireless network edge.
1 code implementation • ECCV 2020 • Tony Ng, Vassileios Balntas, Yurun Tian, Krystian Mikolajczyk
One is focused on second-order spatial information to increase the performance of image descriptors, both local and global.
no code implementations • 9 Jan 2020 • Roy Miles, Krystian Mikolajczyk
Deep neural networks have demonstrated state-of-the-art performance for feature-based image matching through the advent of new large and diverse datasets.
1 code implementation • 22 Nov 2019 • Adrian Lopez Rodriguez, Krystian Mikolajczyk
For the first step, we use a style transfer method for pixel-adaptation of source images to the target domain.
no code implementations • 28 Oct 2019 • Mikolaj Jankowski, Deniz Gunduz, Krystian Mikolajczyk
Motivated by surveillance applications with wireless cameras or drones, we consider the problem of image retrieval over a wireless channel.
3 code implementations • ICCV 2019 • Axel Barroso-Laguna, Edgar Riba, Daniel Ponsa, Krystian Mikolajczyk
We introduce a novel approach for keypoint detection task that combines handcrafted and learned CNN filters within a shallow multi-scale architecture.
Ranked #5 on Image Matching on IMC PhotoTourism (using extra training data)
no code implementations • 30 Mar 2019 • Sara Iodice, Krystian Mikolajczyk
Inspired by the effectiveness of adversarial training in the area of Generative Adversarial Networks we present a new approach for learning feature representations in person re-identification.
Ranked #49 on Person Re-Identification on Market-1501
no code implementations • 24 Jul 2018 • Sara Iodice, Krystian Mikolajczyk
We propose Partial Matching Net (PMN) that detects body joints, aligns partial views and hallucinates the missing parts based on the information present in the frame and a learned model of a person.
1 code implementation • 16 May 2018 • Athanasios Vlontzos, Krystian Mikolajczyk
In interventional radiology, short video sequences of vein structure in motion are captured in order to help medical personnel identify vascular issues or plan intervention.
no code implementations • 3 Oct 2017 • Fei Yan, Krystian Mikolajczyk, Josef Kittler
We propose a joint vision and language model based on CCA and CNN architectures to match across the two modalities as well as to enrich visual examples for which there are no language descriptions.
no code implementations • CVPR 2017 • Vassileios Balntas, Karel Lenc, Andrea Vedaldi, Krystian Mikolajczyk
In this paper, we propose a novel benchmark for evaluating local image descriptors.
no code implementations • WS 2017 • Arnau Ramisa, Fei Yan, Francesc Moreno-Noguer, Krystian Mikolajczyk
We present BreakingNews, a novel dataset with approximately 100K news articles including images, text and captions, and enriched with heterogeneous meta-data (e. g. GPS coordinates and popularity metrics).
no code implementations • 23 Mar 2016 • Arnau Ramisa, Fei Yan, Francesc Moreno-Noguer, Krystian Mikolajczyk
Deep Canonical Correlation Analysis is deployed for article illustration, and a new loss function based on Great Circle Distance is proposed for geolocation.
1 code implementation • 19 Jan 2016 • Vassileios Balntas, Edward Johns, Lilian Tang, Krystian Mikolajczyk
We address this problem and propose a CNN based descriptor with improved matching performance, significantly reduced training and execution time, as well as low dimensionality.
no code implementations • CVPR 2015 • Fei Yan, Krystian Mikolajczyk
This paper addresses the problem of matching images and captions in a joint latent space learnt with deep canonical correlation analysis (DCCA).
no code implementations • CVPR 2015 • Vassileios Balntas, Lilian Tang, Krystian Mikolajczyk
The patch adapted descriptors are then efficiently built online from a subset of tests which lead to lower intra class distances thus a more robust descriptor.