Search · four archives
Search · four archives
25 papers · ranked by Valyu relevance
Cuong Manh Hoang
Instance segmentation is essential for numerous computer vision applications, including robotics, human-computer interaction, and autonomous driving. Currently, popular models bring impressive performance in instance segmentation by training with a large number of human annotations, which are costly to collect. For…
Dantong Niu, Xudong Wang, Xinyang Han, Long Lian + 2 more
Although CutLER provides high-quality instance segmentation masks without human annotations, the predicted masks are class-agnostic, and thus do not include semantic labels for each instance. Our method addresses this issue by grouping the detected instances with a clustering method. In this way, instances assigned to…
Paul Engstler, Luke Melas-Kyriazi, Christian Rupprecht, Iro Laina
Self-supervised learning (SSL) can be used to solve complex visual tasks without human labels. Self-supervised representations encode useful semantic information about images, and as a result, they have already been used for tasks such as unsupervised semantic segmentation. In this paper, we investigate self-supervised…
Farnoosh Arefi, Amir M. Mansourian, Shohreh Kasaei, Yawen Lu
Recently, there has been growing interest in deep spectral methods for image localization and segmentation, influenced by traditional spectral segmentation approaches. These methods reframe the image decomposition process as a graph partitioning task by extracting features using self-supervised learning and utilizing…
Xudong Wang, Jingfeng Yang, Trevor Darrell
The Segmentation Anything Model (SAM) requires labor-intensive data labeling. We present Unsupervised SAM (UnSAM) for promptable and automatic wholeimage segmentation that does not require human annotations. UnSAM utilizes a divide-and-conquer strategy to "discover" the hierarchical structure of visual scenes. We first…
Wu, Ziling, Moemeni, Armaghan + 1 more
Unsupervised object discovery (UOD) aims to detect and segment objects in 2D images without handcrafted annotations. Recent progress in self-supervised representation learning [9, [66]] has led to some success in UOD algorithms [21, 53, [69]]. However, the absence of ground truth provides existing UOD methods with two…
Bogdan Kochetov, Phoenix Bell, Paulo S. Garcia, Akram S. Shalaby + 12 more
Multiplexed imaging technologies have made it possible to interrogate complex tumor microenvironments at sub-cellular resolution within their native spatial context. However, proper quantification of this complexity requires the ability to easily and accurately segment cells into their sub-cellular compartments. Within…
Bogdan Kochetov, Phoenix Bell, Paulo S. Garcia, Akram S. Shalaby + 12 more
'Rebecca Raphael' 'Benjamin Raymond' 'Brian J. Leibowitz' 'Karen Schoedel' 'Rhonda M. Brand' 'Randall E. Brand' 'Jian Yu' 'Lin Zhang' 'Brenda Diergaarde' 'Robert E. Schoen' 'Aatur Singhi' 'Shikhar Uttam'] Multiplexed imaging technologies have made it possible to interrogate complex tumor microenvironments at…
Stephan Ihle, Andreas M. Reichmuth, Sophie Girardin, Hana Han + 5 more
The segmentation of images is a common task in a broad range of research fields. To tackle increasingly complex images, artificial intelligence (AI) based approaches have emerged to overcome the shortcomings of traditional feature detection methods. Owing to the fact that most AI research is made publicly accessible…
Trung Pham, Vijay Kumar B G, Thanh-Toan Do, Gustavo Carneiro + 1 more
'Ian Reid'] Abstract. This paper addresses the semantic instance segmentation task in the open-set conditions, where input images can contain known and unknown object classes. The training process of existing semantic instance segmentation methods requires annotation masks for all object instances, which is expensive…
Zhenyu Wang, Yali Li, Shengjin Wang
Current instance segmentation methods rely heavily on pixel-level annotated images. The huge cost to obtain such fully-annotated images restricts the dataset scale and limits the performance. In this paper, we formally address semisupervised instance segmentation, where unlabeled images are employed to boost the…
Iman Aganj, Mukesh G. Harisinghani, Ralph Weissleder, Bruce Fischl
Image segmentation is a critical step in numerous medical imaging studies, which can be facilitated by automatic computational techniques. Supervised methods, although highly effective, require large training datasets of manually labeled images that are labor-intensive to produce. Unsupervised methods, on the contrary…
Shintaro Miyaki, Takashi Morikura, Shori Nishimoto, Yuta Tokuoka + 2 more
Semantic cell segmentation from microscopic images is essential for the quantitative evaluation of cell morphology. Although supervised deep-learning-based models offer accurate segmentation, their performance degrades for unknown cell types. To address this problem, unsupervised domain adaptation methods based on…
Ke Niu, Jiadong Guo, Ming Zhang, Zhongmin Guo + 4 more
Introduction Brain tissue segmentation in magnetic resonance imaging (MRI) is a fundamental step in quantitative neuroimaging and supports the analysis of structural brain alterations associated with aging. However, voxel-level annotation is expensive and labor-intensive, which limits the development of artificial…
Paulo Victor dos Santos, Marcella Scoczynski Ribeiro Martins, Solange Amorim Nogueira, Cristhiane Gonçalves + 3 more
'Solange Amorim Nogueira' 'Cristhiane Gonçalves' 'Rafael Maffei Loureiro' 'Wesley Pacheco Calixto' 'Ihssan S. Masad'] This article presents an unsupervised method for segmenting brain computed tomography scans. The proposed methodology involves image feature extraction and application of similarity and continuity…
Mariana Belgiu, Lucian Drǎguţ
Although multiresolution segmentation (MRS) is a powerful technique for dealing with very high resolution imagery, some of the image objects that it generates do not match the geometries of the target objects, which reduces the classification accuracy. MRS can, however, be guided to produce results that approach the…
Áron Fóthi, Adrián Szlatincsán, Ellák Somfai, Yun Zhang
A novel approach for video instance segmentation is presented using semisupervised learning. Our Cluster2Former model leverages scribble-based annotations for training, significantly reducing the need for comprehensive pixel-level masks. We augment a video instance segmenter, for example, the Mask2Former architecture…
Matthew R Whiteway, Evan S Schaffer, Anqi Wu, E Kelly Buchanan + 3 more
A popular approach to quantifying animal behavior from video data is through discrete behavioral segmentation, wherein video frames are labeled as containing one or more behavior classes such as walking or grooming. Sequence models learn to map behavioral features extracted from video frames to discrete behaviors, and…
Akhmedkhan Shabanov, Daja Schichler, Constantin Pape, Sara Cuylen-Haering + 1 more
We introduce a simple mechanism by which a CNN trained to perform semantic segmentation of individual images can be re-trained - with no additional annotations - to improve its performance for segmentation of videos. We put the segmentation CNN in a Siamese setup with shared weights and train both for segmentation…
Rama El-khawaldeh, Mason Guy, Finn Bork, Nina Taherimakhsousi + 6 more
This work presents a generalizable computer vision (CV) and machine learning model that is used for automated real-time monitoring and control of a diverse array of workup processes. Our system simultaneously monitors multiple physical parameters (e.g., liquid level, homogeneity, turbidity, solid, residue, and color)…
Authors not listed
X-ray diffraction (XRD) is an immediate and powerful characterization technique that provides detailed information on the lattice structure and long-range order in crystalline materials. In recent decades, the quality and quantity of available crystal structure data has exploded, in large part due to the advent of…
Alexander E. Siemenn, Eunice Aissi, Fang Sheng, Armi Tiihonen + 3 more
In materials research, the task of characterizing hundreds of different materials traditionally requires equally many human hours spent measuring samples one by one. We demonstrate that with the integration of computer vision into this material research workflow, many of these tasks can be automated, significantly…
Authors not listed
The analysis of nonadiabatic molecular dynamics (NAMD) data presents significant challenges due to its high dimensionality and complexity. To address these issues, we introduce ULaMDyn, a Python-based, open-source package designed to automate the unsupervised analysis of large datasets generated by NAMD simulations.…
Authors not listed
Recent advances in artificial intelligence have significantly improved spectral data analysis. In this study, we used unsupervised machine learning to classify chemical compounds based on infrared (IR) spectral images, without relying on prior chemical knowledge. The potential of machine learning for chemical…
Authors not listed
The exponential growth of chemical literature necessitates the development of automated tools for extracting and curating molecular information from unstructured scientific publications into open-access chemical databases. Current optical chemical structure recognition (OCSR) and named entity recognition solutions…