Learn to Generalize and Adapt across Domains in Semantic Segmentation

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Reimagine BiSeNet for Real-Time Domain Adaptation in Semantic Segmentation

Semantic segmentation models have reached remarkable performance across various tasks. However, this performance is achieved with extremely large models, using powerful computational resources and without considering training and inference time. Real-world applications, on the other hand, necessitate models with minimal memory demands, efficient inference speed, and executable with low-resources embedded devices, such as self-driving vehicles. In this paper, we look at the challenge of real-time semantic segmentation across domains, and we train a model to act appropriately on real-world data even though it was trained on a synthetic realm. We employ a new lightweight and shallow discriminator that was specifically created for this purpose. To the best of our knowledge, we are the first to present a real-time adversarial approach for assessing the domain adaption problem in semantic segmentation. We tested our framework in the two standard protocol: GTA5 to Cityscapes and SYNTHIA to Cityscapes. Code is available at: https://github.com/taveraantonio/RTDA.Comment: Accepted at I-RIM 3D 202

Augmentation Invariance and Adaptive Sampling in Semantic Segmentation of Agricultural Aerial Images

In this paper, we investigate the problem of Semantic Segmentation for agricultural aerial imagery. We observe that the existing methods used for this task are designed without considering two characteristics of the aerial data: (i) the top-down perspective implies that the model cannot rely on a fixed semantic structure of the scene, because the same scene may be experienced with different rotations of the sensor; (ii) there can be a strong imbalance in the distribution of semantic classes because the relevant objects of the scene may appear at extremely different scales (e.g., a field of crops and a small vehicle). We propose a solution to these problems based on two ideas: (i) we use together a set of suitable augmentation and a consistency loss to guide the model to learn semantic representations that are invariant to the photometric and geometric shifts typical of the top-down perspective (Augmentation Invariance); (ii) we use a sampling method (Adaptive Sampling) that selects the training images based on a measure of pixel-wise distribution of classes and actual network confidence. With an extensive set of experiments conducted on the Agriculture-Vision dataset, we demonstrate that our proposed strategies improve the performance of the current state-of-the-art method.Comment: CVPR 2022 Workshop - Agriculture Visio

IDDA: a large-scale multi-domain dataset for autonomous driving

Semantic segmentation is key in autonomous driving. Using deep visual learning architectures is not trivial in this context, because of the challenges in creating suitable large scale annotated datasets. This issue has been traditionally circumvented through the use of synthetic datasets, that have become a popular resource in this field. They have been released with the need to develop semantic segmentation algorithms able to close the visual domain shift between the training and test data. Although exacerbated by the use of artificial data, the problem is extremely relevant in this field even when training on real data. Indeed, weather conditions, viewpoint changes and variations in the city appearances can vary considerably from car to car, and even at test time for a single, specific vehicle. How to deal with domain adaptation in semantic segmentation, and how to leverage effectively several different data distributions (source domains) are important research questions in this field. To support work in this direction, this paper contributes a new large scale, synthetic dataset for semantic segmentation with more than 100 different source visual domains. The dataset has been created to explicitly address the challenges of domain shift between training and test data in various weather and view point conditions, in seven different city types. Extensive benchmark experiments assess the dataset, showcasing open challenges for the current state of the art. The dataset will be available at: https://idda-dataset.github.io/home/ .Comment: Accepted at IROS 2020 and RA-L. Download at: https://idda-dataset.github.io/home

Plan de negocio para el desarrollo, producción, comercialización y reutilización de bolsas plásticas en la ciudad de Bogotá

EmprendimientoEste trabajo se realiza con el fin de crear una empresa legalmente constituida que produzca, comercialice y distribuya en la ciudad de Bogotá bolsas de polietileno, para esto se determinan una serie de etapas los cuales permitirán el desarrollo adecuado de este plan de emprendimiento, dichos objetivos son; estudio de mercado, estudio técnico, estudia administrativo - legal y el estudio financiero que denotara si el proyecto tiene gran rentabilidad.INTRODUCCIÓN 1. GENERALIDADES 2. ESTUDIO DE MERCADO 3. ESTUDIO TECNICO 4. ESTUDIO ADMINISTRATIVO Y LEGALES 5. ESTUDIO FINANCIERO 6. CONCLUSIONES 7. RECOMENDACIONES BIBLIOGRAFÍA ANEXOSPregradoIngeniero Industria

Adaptación del mango Keitt (Mangifera indica L).a las condiciones agroecológicas en San Vicente de Chucuri Santander.

presente proyecto se investigó el crecimiento, desarrollo y adaptabilidad del mango Keitt (Mangifera índica L) en el corregimiento de Albania en el municipio de San Vicente de Chucurí, como una alternativa agrícola que permitan el fortalecimiento y la creación de la empresa campesina y encadenamiento comercial que harán partes integral del proceso agroecológico.Evaluar la adaptación del crecimiento y desarrollo del mango keitt (Mangifera indica L) a las condiciones agroecológicas en San Vicente de Chucuri Santander, en condiciones de variabilidad climatológica de temperatura, humedad relativa, brillo solar, precipitación y suelo e condiciones naturales.o evaluate the adaptation of the growth and development of the mango keitt (Mangifera indica L) to the agroecological conditions in San Vicente de Chucuri Santander, in conditions of climatic variability of temperature, relative humidity, solar brightness, precipitation and soil and natural conditions

Incremental Learning in Semantic Segmentation from Image Labels

Although existing semantic segmentation approaches achieve impressive results, they still struggle to update their models incrementally as new categories are uncovered. Furthermore, pixel-by-pixel annotations are expensive and time-consuming. This paper proposes a novel framework for Weakly Incremental Learning for Semantic Segmentation, that aims at learning to segment new classes from cheap and largely available image-level labels. As opposed to existing approaches, that need to generate pseudolabels offline, we use a localizer, trained with image-level labels and regularized by the segmentation model, to obtain pseudo-supervision online and update the model incrementally. We cope with the inherent noise in the process by using soft-labels generated by the localizer. We demonstrate the effectiveness of our approach on the Pascal VOC and COCO datasets, outperforming offline weakly-supervised methods and obtaining results comparable with incremental learning methods with full supervision. 1 1 Code can be found at https://github.com/fcd194/WILSON