9 research outputs found
Context-self contrastive pretraining for crop type semantic segmentation
In this paper, we propose a fully supervised pre-training scheme based on
contrastive learning particularly tailored to dense classification tasks. The
proposed Context-Self Contrastive Loss (CSCL) learns an embedding space that
makes semantic boundaries pop-up by use of a similarity metric between every
location in a training sample and its local context. For crop type semantic
segmentation from Satellite Image Time Series (SITS) we find performance at
parcel boundaries to be a critical bottleneck and explain how CSCL tackles the
underlying cause of that problem, improving the state-of-the-art performance in
this task. Additionally, using images from the Sentinel-2 (S2) satellite
missions we compile the largest, to our knowledge, SITS dataset densely
annotated by crop type and parcel identities, which we make publicly available
together with the data generation pipeline. Using that data we find CSCL, even
with minimal pre-training, to improve all respective baselines and present a
process for semantic segmentation at super-resolution for obtaining crop
classes at a more granular level. The code and instructions to download the
data can be found in https://github.com/michaeltrs/DeepSatModels.Comment: 15 pages, 17 figure
Screened poisson hyperfields for shape coding
We present a novel perspective on shape characterization using the screened Poisson equation. We discuss that the effect of the screening parameter is a change of measure of the underlying metric space. Screening also indicates a conditioned random walker biased by the choice of measure. A continuum of shape fields is created by varying the screening parameter or, equivalently, the bias of the random walker. In addition to creating a regional encoding of the diffusion with a different bias, we further break down the influence of boundary interactions by considering a number of independent random walks, each emanating from a certain boundary point, whose superposition yields the screened Poisson field. Probing the screened Poisson equation from these two complementary perspectives leads to a high-dimensional hyperfield: a rich characterization of the shape that encodes global, local, interior, and boundary interactions. To extract particular shape information as needed in a compact way from the hyperfield, we apply various decompositions either to unveil parts of a shape or parts of a boundary or to create consistent mappings. The latter technique involves lower-dimensional embeddings, which we call screened Poisson encoding maps (SPEM). The expressive power of the SPEM is demonstrated via illustrative experiments as well as a quantitative shape retrieval experiment over a public benchmark database on which the SPEM method shows a high-ranking performance among the existing state-of-the-art shape retrieval methods
iNVS: Repurposing Diffusion Inpainters for Novel View Synthesis
We present a method for generating consistent novel views from a single
source image. Our approach focuses on maximizing the reuse of visible pixels
from the source image. To achieve this, we use a monocular depth estimator that
transfers visible pixels from the source view to the target view. Starting from
a pre-trained 2D inpainting diffusion model, we train our method on the
large-scale Objaverse dataset to learn 3D object priors. While training we use
a novel masking mechanism based on epipolar lines to further improve the
quality of our approach. This allows our framework to perform zero-shot novel
view synthesis on a variety of objects. We evaluate the zero-shot abilities of
our framework on three challenging datasets: Google Scanned Objects, Ray Traced
Multiview, and Common Objects in 3D. See our webpage for more details:
https://yashkant.github.io/invs/Comment: Accepted to SIGGRAPH Asia, 2023 (Conference Papers
Apprentissage de Correspondances Image-Surface
This thesis addresses the task of establishing adense correspondence between an image and a 3Dobject template. We aim to bring vision systemscloser to a surface-based 3D understanding ofobjects by extracting information that iscomplementary to existing landmark- or partbasedrepresentations.We use convolutional neural networks (CNNs)to densely associate pixels with intrinsiccoordinates of 3D object templates. Through theestablished correspondences we effortlesslysolve a multitude of visual tasks, such asappearance transfer, landmark localization andsemantic segmentation by transferring solutionsfrom the template to an image. We show thatgeometric correspondence between an imageand a 3D model can be effectively inferred forboth the human face and the human body.Cette thĂšse se concentre sur le dĂ©veloppement demodĂšles de reprĂ©sentation dense dâobjets 3-D Ă partir dâimages. Lâobjectif de ce travail estdâamĂ©liorer les modĂšles surfaciques 3-D fournispar les systĂšmes de vision par ordinateur, enutilisant de nouveaux Ă©lĂ©ments tirĂ©s des images,plutĂŽt que les annotations habituellementutilisĂ©es, ou que les modĂšles basĂ©s sur unedivision de lâobjet en diffĂ©rents parties.Des rĂ©seaux neuronaux convolutifs (CNNs) sontutilisĂ©s pour associer de maniĂšre dense les pixelsdâune image avec les coordonnĂ©es 3-D dâunmodĂšle de lâobjet considĂ©rĂ©. Cette mĂ©thodepermet de rĂ©soudre trĂšs simplement unemultitude de tĂąches de vision par ordinateur,telles que le transfert dâapparence, la localisationde repĂšres ou la segmentation sĂ©mantique, enutilisant la correspondance entre une solution surle modĂšle surfacique 3-D et lâimage 2-DconsidĂ©rĂ©e. On dĂ©montre quâune correspondancegĂ©omĂ©trique entre un modĂšle 3-D et une imagepeut ĂȘtre Ă©tablie pour le visage et le corpshumains
DenseReg: fully convolutional dense shape regression in-the-wild
In this paper we propose to learn a mapping from image pixels into a dense template grid through a fully convolutional network. We formulate this task as a regression problem and train our network by leveraging upon manually annotated facial landmarks âin-the-wildâ. We use such landmarks to establish a dense correspondence field between a three-dimensional object template and the input image, which then serves as the ground-truth for training our regression system. We show that we can combine ideas from semantic segmentation with regression networks, yielding a highly-accurate âquantized regressionâ architecture. Our system, called DenseReg, allows us to estimate dense image-to-template correspondences in a fully convolutional manner. As such our network can provide useful correspondence information as a stand-alone system, while when used as an initialization for Statistical Deformable Models we obtain landmark localization results that largely outperform the current state-of-the-art on the challenging 300W benchmark. We thoroughly evaluate our method on a host of facial analysis tasks, and demonstrate its use for other correspondence estimation tasks, such as the human body and the human ear. DenseReg code is made available at http://alpguler.com/DenseReg.html along with supplementary materials
Push and Pull Factors of Why Medical Students Want to Leave TĂŒrkiye: A Countrywide Multicenter Study
Phenomenon: Physician immigration from other countries is increasing as developed countries continue to be desirable destinations for physicians; however, the determinants of Turkish physiciansâ migration decisions are still unclear. Despite its wide coverage in the media and among physicians in TĂŒrkiye, and being the subject of much debate, there is insufficient data to justify this attention. With this study, we aimed to investigate the tendency of senior medical students in TĂŒrkiye to pursue their professional careers abroad and its related factors. Approach: This cross-sectional study involved 9881 senior medical students from 39 different medical schools in TĂŒrkiye in 2022. Besides participantsâ migration decision, we evaluated the push and pull factors related to working, social environment and lifestyle in TĂŒrkiye and abroad, medical school education inadequacy, and personal insufficiencies, as well as the socioeconomic variables that may affect the decision to migrate abroad. The analyses were carried out with a participation rate of at least 50%. Findings: Of the medical students, 70.7% had emigration intentions. Approximately 60% of those want to stay abroad permanently, and 61.5% of them took initiatives such as learning a foreign language abroad (54.5%) and taking relevant exams (18.9%). Those who wanted to work in the field of Research & Development were 1.37 (95% CI: 1.22â1.54) times more likely to emigrate. The push factor that was related to emigration intention was the âworking conditions in the countryâ (OR: 1.89, 95% CI: 1.56â2.28) whereas the âsocial environment/lifestyle abroadâ was the mere pull factor for the tendency of emigration (OR: 1.73, 95% CI: 1.45â2.06). In addition, the quality problem in medical schools also had a significant impact on studentsâ decisions (OR: 2.20, 95% CI: 1.83â2.65). Insights: Although the percentage of those who want to emigrate âdefinitelyâ was at the same level as in the other developing countries, the tendency to migrate âpermanentlyâ was higher in TĂŒrkiye. Improving working conditions in the country and increasing the quality of medical faculties seem vital in preventing the migration of physicians