27,173 research outputs found
Graph Networks for Multi-Label Image Recognition
Providing machines with a robust visualization of multiple objects in a scene has a myriad of applications in the physical world. This research solves the task of multi-label image recognition using a deep learning approach. For most multi-label image recognition datasets, there are multiple objects within a single image and a single label can be seen many times throughout the dataset. Therefore, it is not efficient to classify each object in isolation, rather it is important to infer the inter-dependencies between the labels. To extract a latent representation of the pixels from an image, this work uses a convolutional network approach evaluating three different image feature extraction networks. In order to learn the label inter-dependencies, this work proposes a graph convolution network approach as compared to previous approaches such as probabilistic graph or recurrent neural networks. In the graph neural network approach, the image labels are first encoded into word embeddings. These serve as nodes on a graph. The correlations between these nodes are learned using graph neural networks. We investigate how to create the adjacency matrix without manual calculation of the label correlations in the respective datasets. This proposed approach is evaluated on the widely-used PASCAL VOC, MSCOCO, and NUS-WIDE multi-label image recognition datasets. The main evaluation metrics used will be mean average precision and overall F1 score, to show that the learned adjacency matrix method for labels along with the addition of visual attention for image features is able to achieve similar performance to manually calculating the label adjacency matrix
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
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