783 research outputs found
Multi-task deep learning models in visual fashion understanding
Visual fashion understanding (VFU) is a discipline which aims to solve tasks related to clothing recognition, such as garment categorization, garment’s attributes prediction or clothes retrieval, with the use of computer vision algorithms trained on fashion-related data. Having surveyed VFU- related scientific literature, I conclude that, because of the fact that at the heart of all VFU tasks is the same issue of visually understanding garments, those VFU tasks are in fact related. I present a hypothesis that building larger multi-task learning models dedicated to predicting multiple VFU tasks at once might lead to better generalization properties of VFU models. I assess the validity of my hypothesis by implementing two deep learning solutions dedicated primarily to category and attribute prediction. First solution uses multi-task learning concept of sharing features from ad- ditional branch dedicated to localization task of landmarks’ position prediction. Second solution does not share knowledge from localization branch. Comparison of those two implementations con- firmed my hypothesis, as sharing knowledge between tasks increased category prediction accuracy by 53% and attributes prediction recall by 149%. I conclude that multi-task learning improves generalization properties of deep learning-based visual fashion understanding models across tasks
Pedestrian Attribute Recognition: A Survey
Recognizing pedestrian attributes is an important task in computer vision
community due to it plays an important role in video surveillance. Many
algorithms has been proposed to handle this task. The goal of this paper is to
review existing works using traditional methods or based on deep learning
networks. Firstly, we introduce the background of pedestrian attributes
recognition (PAR, for short), including the fundamental concepts of pedestrian
attributes and corresponding challenges. Secondly, we introduce existing
benchmarks, including popular datasets and evaluation criterion. Thirdly, we
analyse the concept of multi-task learning and multi-label learning, and also
explain the relations between these two learning algorithms and pedestrian
attribute recognition. We also review some popular network architectures which
have widely applied in the deep learning community. Fourthly, we analyse
popular solutions for this task, such as attributes group, part-based,
\emph{etc}. Fifthly, we shown some applications which takes pedestrian
attributes into consideration and achieve better performance. Finally, we
summarized this paper and give several possible research directions for
pedestrian attributes recognition. The project page of this paper can be found
from the following website:
\url{https://sites.google.com/view/ahu-pedestrianattributes/}.Comment: Check our project page for High Resolution version of this survey:
https://sites.google.com/view/ahu-pedestrianattributes
Re-identification and semantic retrieval of pedestrians in video surveillance scenarios
Person re-identification consists of recognizing individuals across different sensors of a camera
network. Whereas clothing appearance cues are widely used, other modalities could
be exploited as additional information sources, like anthropometric measures and gait. In
this work we investigate whether the re-identification accuracy of clothing appearance descriptors
can be improved by fusing them with anthropometric measures extracted from
depth data, using RGB-Dsensors, in unconstrained settings. We also propose a dissimilaritybased
framework for building and fusing multi-modal descriptors of pedestrian images for
re-identification tasks, as an alternative to the widely used score-level fusion. The experimental
evaluation is carried out on two data sets including RGB-D data, one of which is a
novel, publicly available data set that we acquired using Kinect sensors.
In this dissertation we also consider a related task, named semantic retrieval of pedestrians
in video surveillance scenarios, which consists of searching images of individuals using
a textual description of clothing appearance as a query, given by a Boolean combination of
predefined attributes. This can be useful in applications like forensic video analysis, where
the query can be obtained froma eyewitness report. We propose a general method for implementing
semantic retrieval as an extension of a given re-identification system that uses any
multiple part-multiple component appearance descriptor. Additionally, we investigate on
deep learning techniques to improve both the accuracy of attribute detectors and generalization
capabilities. Finally, we experimentally evaluate our methods on several benchmark
datasets originally built for re-identification task
Fashion-Oriented Image Captioning with External Knowledge Retrieval and Fully Attentive Gates
Research related to fashion and e-commerce domains is gaining attention in computer vision and multimedia communities. Following this trend, this article tackles the task of generating fine-grained and accurate natural language descriptions of fashion items, a recently-proposed and under-explored challenge that is still far from being solved. To overcome the limitations of previous approaches, a transformer-based captioning model was designed with the integration of external textual memory that could be accessed through k-nearest neighbor (kNN) searches. From an architectural point of view, the proposed transformer model can read and retrieve items from the external memory through cross-attention operations, and tune the flow of information coming from the external memory thanks to a novel fully attentive gate. Experimental analyses were carried out on the fashion captioning dataset (FACAD) for fashion image captioning, which contains more than 130k fine-grained descriptions, validating the effectiveness of the proposed approach and the proposed architectural strategies in comparison with carefully designed baselines and state-of-the-art approaches. The presented method constantly outperforms all compared approaches, demonstrating its effectiveness for fashion image captioning
- …