Zobrazeno 1 - 10
of 134
pro vyhledávání: '"Bilen, Hakan"'
Autor:
Goldsborough, Thibaut, Philps, Ben, O'Callaghan, Alan, Inglis, Fiona, Leplat, Leo, Filby, Andrew, Bilen, Hakan, Bankhead, Peter
Cell and nucleus segmentation are fundamental tasks for quantitative bioimage analysis. Despite progress in recent years, biologists and other domain experts still require novel algorithms to handle increasingly large and complex real-world datasets.
Externí odkaz:
http://arxiv.org/abs/2408.15954
This paper introduces a novel anomaly detection (AD) problem that focuses on identifying `odd-looking' objects relative to the other instances within a scene. Unlike the traditional AD benchmarks, in our setting, anomalies in this context are scene-s
Externí odkaz:
http://arxiv.org/abs/2406.20099
Automatic anomaly detection based on visual cues holds practical significance in various domains, such as manufacturing and product quality assessment. This paper introduces a new conditional anomaly detection problem, which involves identifying anom
Externí odkaz:
http://arxiv.org/abs/2406.19393
We propose a novel unsupervised method to learn the pose and part-segmentation of articulated objects with rigid parts. Given two observations of an object in different articulation states, our method learns the geometry and appearance of object part
Externí odkaz:
http://arxiv.org/abs/2406.16623
We focus on the problem of recognising the end state of an action in an image, which is critical for understanding what action is performed and in which manner. We study this focusing on the task of predicting the coarseness of a cut, i.e., deciding
Externí odkaz:
http://arxiv.org/abs/2405.07723
Publikováno v:
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024, pp. 19521-19530
Recent progress in self-supervised representation learning has resulted in models that are capable of extracting image features that are not only effective at encoding image level, but also pixel-level, semantics. These features have been shown to be
Externí odkaz:
http://arxiv.org/abs/2312.13216
In this paper, we study multimodal coreference resolution, specifically where a longer descriptive text, i.e., a narration is paired with an image. This poses significant challenges due to fine-grained image-text alignment, inherent ambiguity present
Externí odkaz:
http://arxiv.org/abs/2310.13619
Deep neural networks have become a standard building block for designing models that can perform multiple dense computer vision tasks such as depth estimation and semantic segmentation thanks to their ability to capture complex correlations in high d
Externí odkaz:
http://arxiv.org/abs/2310.00986
We introduce Explicit Neural Surfaces (ENS), an efficient smooth surface representation that directly encodes topology with a deformation field from a known base domain. We apply this representation to reconstruct explicit surfaces from multiple view
Externí odkaz:
http://arxiv.org/abs/2306.02956
The goal of this work is to understand the way actions are performed in videos. That is, given a video, we aim to predict an adverb indicating a modification applied to the action (e.g. cut "finely"). We cast this problem as a regression task. We mea
Externí odkaz:
http://arxiv.org/abs/2303.15086