3,548 research outputs found
Muti-Stage Hierarchical Food Classification
Food image classification serves as a fundamental and critical step in
image-based dietary assessment, facilitating nutrient intake analysis from
captured food images. However, existing works in food classification
predominantly focuses on predicting 'food types', which do not contain direct
nutritional composition information. This limitation arises from the inherent
discrepancies in nutrition databases, which are tasked with associating each
'food item' with its respective information. Therefore, in this work we aim to
classify food items to align with nutrition database. To this end, we first
introduce VFN-nutrient dataset by annotating each food image in VFN with a food
item that includes nutritional composition information. Such annotation of food
items, being more discriminative than food types, creates a hierarchical
structure within the dataset. However, since the food item annotations are
solely based on nutritional composition information, they do not always show
visual relations with each other, which poses significant challenges when
applying deep learning-based techniques for classification. To address this
issue, we then propose a multi-stage hierarchical framework for food item
classification by iteratively clustering and merging food items during the
training process, which allows the deep model to extract image features that
are discriminative across labels. Our method is evaluated on VFN-nutrient
dataset and achieve promising results compared with existing work in terms of
both food type and food item classification.Comment: accepted for ACM MM 2023 Madim
DeltaGAN: Towards Diverse Few-shot Image Generation with Sample-Specific Delta
Learning to generate new images for a novel category based on only a few
images, named as few-shot image generation, has attracted increasing research
interest. Several state-of-the-art works have yielded impressive results, but
the diversity is still limited. In this work, we propose a novel Delta
Generative Adversarial Network (DeltaGAN), which consists of a reconstruction
subnetwork and a generation subnetwork. The reconstruction subnetwork captures
intra-category transformation, i.e., "delta", between same-category pairs. The
generation subnetwork generates sample-specific "delta" for an input image,
which is combined with this input image to generate a new image within the same
category. Besides, an adversarial delta matching loss is designed to link the
above two subnetworks together. Extensive experiments on five few-shot image
datasets demonstrate the effectiveness of our proposed method
Engineering Crowdsourced Stream Processing Systems
A crowdsourced stream processing system (CSP) is a system that incorporates
crowdsourced tasks in the processing of a data stream. This can be seen as
enabling crowdsourcing work to be applied on a sample of large-scale data at
high speed, or equivalently, enabling stream processing to employ human
intelligence. It also leads to a substantial expansion of the capabilities of
data processing systems. Engineering a CSP system requires the combination of
human and machine computation elements. From a general systems theory
perspective, this means taking into account inherited as well as emerging
properties from both these elements. In this paper, we position CSP systems
within a broader taxonomy, outline a series of design principles and evaluation
metrics, present an extensible framework for their design, and describe several
design patterns. We showcase the capabilities of CSP systems by performing a
case study that applies our proposed framework to the design and analysis of a
real system (AIDR) that classifies social media messages during time-critical
crisis events. Results show that compared to a pure stream processing system,
AIDR can achieve a higher data classification accuracy, while compared to a
pure crowdsourcing solution, the system makes better use of human workers by
requiring much less manual work effort
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