Zobrazeno 1 - 10
of 21
pro vyhledávání: '"Dutta, Ujjal Kr"'
Foundation Industries (FIs) constitute glass, metals, cement, ceramics, bulk chemicals, paper, steel, etc. and provide crucial, foundational materials for a diverse set of economically relevant industries: automobiles, machinery, construction, househ
Externí odkaz:
http://arxiv.org/abs/2308.16089
Autor:
Dutta, Ujjal Kr
While deep Embedding Learning approaches have witnessed widespread success in multiple computer vision tasks, the state-of-the-art methods for representing natural images need not necessarily perform well on images from other domains, such as paintin
Externí odkaz:
http://arxiv.org/abs/2208.09698
A satellite image is a remotely sensed image data, where each pixel represents a specific location on earth. The pixel value recorded is the reflection radiation from the earth's surface at that location. Multispectral images are those that capture i
Externí odkaz:
http://arxiv.org/abs/2203.11146
In this paper, we address a crucial problem in fashion e-commerce (with respect to customer experience, as well as revenue): color variants identification, i.e., identifying fashion products that match exactly in their design (or style), but only to
Externí odkaz:
http://arxiv.org/abs/2112.02910
Autor:
Dutta, Ujjal Kr
Object Detection, a fundamental computer vision problem, has paramount importance in smart camera systems. However, a truly reliable camera system could be achieved if and only if the underlying object detection component is robust enough across vary
Externí odkaz:
http://arxiv.org/abs/2112.02891
Distance Metric Learning (DML) seeks to learn a discriminative embedding where similar examples are closer, and dissimilar examples are apart. In this paper, we address the problem of Semi-Supervised DML (SSDML) that tries to learn a metric using a f
Externí odkaz:
http://arxiv.org/abs/2105.05061
In this paper, we utilize deep visual Representation Learning to address an important problem in fashion e-commerce: color variants identification, i.e., identifying fashion products that match exactly in their design (or style), but only to differ i
Externí odkaz:
http://arxiv.org/abs/2104.08581
Have you ever looked at an Instagram model, or a model in a fashion e-commerce web-page, and thought \textit{"Wish I could get a list of fashion items similar to the ones worn by the model!"}. This is what we address in this paper, where we propose a
Externí odkaz:
http://arxiv.org/abs/2008.11638
Popular fashion e-commerce platforms mostly provide details about low-level attributes of an apparel (eg, neck type, dress length, collar type) on their product detail pages. However, customers usually prefer to buy apparel based on their style infor
Externí odkaz:
http://arxiv.org/abs/2008.11662
Metric learning is an important problem in machine learning. It aims to group similar examples together. Existing state-of-the-art metric learning approaches require class labels to learn a metric. As obtaining class labels in all applications is not
Externí odkaz:
http://arxiv.org/abs/2008.09880