672 research outputs found
Real-time human ambulation, activity, and physiological monitoring:taxonomy of issues, techniques, applications, challenges and limitations
Automated methods of real-time, unobtrusive, human ambulation, activity, and wellness monitoring and data analysis using various algorithmic techniques have been subjects of intense research. The general aim is to devise effective means of addressing the demands of assisted living, rehabilitation, and clinical observation and assessment through sensor-based monitoring. The research studies have resulted in a large amount of literature. This paper presents a holistic articulation of the research studies and offers comprehensive insights along four main axes: distribution of existing studies; monitoring device framework and sensor types; data collection, processing and analysis; and applications, limitations and challenges. The aim is to present a systematic and most complete study of literature in the area in order to identify research gaps and prioritize future research directions
Learning Fashion Compatibility with Bidirectional LSTMs
The ubiquity of online fashion shopping demands effective recommendation
services for customers. In this paper, we study two types of fashion
recommendation: (i) suggesting an item that matches existing components in a
set to form a stylish outfit (a collection of fashion items), and (ii)
generating an outfit with multimodal (images/text) specifications from a user.
To this end, we propose to jointly learn a visual-semantic embedding and the
compatibility relationships among fashion items in an end-to-end fashion. More
specifically, we consider a fashion outfit to be a sequence (usually from top
to bottom and then accessories) and each item in the outfit as a time step.
Given the fashion items in an outfit, we train a bidirectional LSTM (Bi-LSTM)
model to sequentially predict the next item conditioned on previous ones to
learn their compatibility relationships. Further, we learn a visual-semantic
space by regressing image features to their semantic representations aiming to
inject attribute and category information as a regularization for training the
LSTM. The trained network can not only perform the aforementioned
recommendations effectively but also predict the compatibility of a given
outfit. We conduct extensive experiments on our newly collected Polyvore
dataset, and the results provide strong qualitative and quantitative evidence
that our framework outperforms alternative methods.Comment: ACM MM 1
Every Color Chromakey
In this paper, we propose a region extraction method using chromakey with a two-tone checker pattern background. The proposed method solves the problem in conventional chromakey techniques that foreground objects become transparent if they have the same color with the background. The adjacency condition between two-tone regions of the background and the geometrical information of the background grid lines are utilized for extracting foreground objects. Experimental results show the effectiveness of the proposed method. Same color (a) Conventional chromakey. Same color as C 1 or C
High-Resolution Virtual Try-On with Misalignment and Occlusion-Handled Conditions
Image-based virtual try-on aims to synthesize an image of a person wearing a
given clothing item. To solve the task, the existing methods warp the clothing
item to fit the person's body and generate the segmentation map of the person
wearing the item before fusing the item with the person. However, when the
warping and the segmentation generation stages operate individually without
information exchange, the misalignment between the warped clothes and the
segmentation map occurs, which leads to the artifacts in the final image. The
information disconnection also causes excessive warping near the clothing
regions occluded by the body parts, so-called pixel-squeezing artifacts. To
settle the issues, we propose a novel try-on condition generator as a unified
module of the two stages (i.e., warping and segmentation generation stages). A
newly proposed feature fusion block in the condition generator implements the
information exchange, and the condition generator does not create any
misalignment or pixel-squeezing artifacts. We also introduce discriminator
rejection that filters out the incorrect segmentation map predictions and
assures the performance of virtual try-on frameworks. Experiments on a
high-resolution dataset demonstrate that our model successfully handles the
misalignment and occlusion, and significantly outperforms the baselines. Code
is available at https://github.com/sangyun884/HR-VITON.Comment: Accepted to ECCV 202
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