Adaptive Multiplane Image Generation from a Single Internet Picture

Learning multiplane images from single views with self-supervision

Generating static novel views from an already captured image is a hard task in computer vision and graphics, in particular when the single input image has dynamic parts such as persons or moving objects. In this paper, we tackle this problem by proposing a new framework, called CycleMPI, that is capable of learning a multiplane image representation from single images through a cyclic training strategy for self-supervision. Our framework does not require stereo data for training, therefore it can be trained with massive visual data from the Internet, resulting in a better generalization capabil…

Midgar: Detection of people through computer vision in the Internet of Things scenarios to improve the security in Smart Cities, Smart Towns, and Smart Homes

Could we use Computer Vision in the Internet of Things for using pictures as sensors? This is the principal hypothesis that we want to resolve. Currently, in order to create safety areas, cities, or homes, people use IP cameras. Nevertheless, this system needs people who watch the camera images, watch the recording after something occurred, or watch when the camera notifies them of any movement. These are the disadvantages. Furthermore, there are many Smart Cities and Smart Homes around the world. This is why we thought of using the idea of the Internet of Things to add a way of automating the…

WESPE: Weakly Supervised Photo Enhancer for Digital Cameras

Low-end and compact mobile cameras demonstrate limited photo quality mainly due to space, hardware and budget constraints. In this work, we propose a deep learning solution that translates photos taken by cameras with limited capabilities into DSLR-quality photos automatically. We tackle this problem by introducing a weakly supervised photo enhancer (WESPE) - a novel image-to-image Generative Adversarial Network-based architecture. The proposed model is trained by under weak supervision: unlike previous works, there is no need for strong supervision in the form of a large annotated dataset of …

群组扩散Transformer是无监督多任务学习器

Group Diffusion Transformers are Unsupervised Multitask Learners

本文提出群组扩散Transformer（GDTs），将多种视觉生成任务统一为群组生成问题，通过跨图像自注意力实现无监督任务无关预训练，在30个任务上取得有竞争力的零样本性能。

Picture-to-Amount (PITA): Predicting Relative Ingredient Amounts from Food Images

Increased awareness of the impact of food consumption on health and lifestyle today has given rise to novel data-driven food analysis systems. Although these systems may recognize the ingredients, a detailed analysis of their amounts in the meal, which is paramount for estimating the correct nutrition, is usually ignored. In this paper, we study the novel and challenging problem of predicting the relative amount of each ingredient from a food image. We propose PITA, the Picture-to-Amount deep learning architecture to solve the problem. More specifically, we predict the ingredient amounts using…

Location Recognition Over Large Time Lags

Would it be possible to automatically associate ancient pictures to modern ones and create fancy cultural heritage city maps? We introduce here the task of recognizing the location depicted in an old photo given modern annotated images collected from the Internet. We present an extensive analysis on different features, looking for the most discriminative and most robust to the image variability induced by large time lags. Moreover, we show that the described task benefits from domain adaptation.