12,800 research outputs found
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
MiniMax Entropy Network: Learning Category-Invariant Features for Domain Adaptation
How to effectively learn from unlabeled data from the target domain is
crucial for domain adaptation, as it helps reduce the large performance gap due
to domain shift or distribution change. In this paper, we propose an
easy-to-implement method dubbed MiniMax Entropy Networks (MMEN) based on
adversarial learning. Unlike most existing approaches which employ a generator
to deal with domain difference, MMEN focuses on learning the categorical
information from unlabeled target samples with the help of labeled source
samples. Specifically, we set an unfair multi-class classifier named
categorical discriminator, which classifies source samples accurately but be
confused about the categories of target samples. The generator learns a common
subspace that aligns the unlabeled samples based on the target pseudo-labels.
For MMEN, we also provide theoretical explanations to show that the learning of
feature alignment reduces domain mismatch at the category level. Experimental
results on various benchmark datasets demonstrate the effectiveness of our
method over existing state-of-the-art baselines.Comment: 8 pages, 6 figure
Belief Tree Search for Active Object Recognition
Active Object Recognition (AOR) has been approached as an unsupervised
learning problem, in which optimal trajectories for object inspection are not
known and are to be discovered by reducing label uncertainty measures or
training with reinforcement learning. Such approaches have no guarantees of the
quality of their solution. In this paper, we treat AOR as a Partially
Observable Markov Decision Process (POMDP) and find near-optimal policies on
training data using Belief Tree Search (BTS) on the corresponding belief Markov
Decision Process (MDP). AOR then reduces to the problem of knowledge transfer
from near-optimal policies on training set to the test set. We train a Long
Short Term Memory (LSTM) network to predict the best next action on the
training set rollouts. We sho that the proposed AOR method generalizes well to
novel views of familiar objects and also to novel objects. We compare this
supervised scheme against guided policy search, and find that the LSTM network
reaches higher recognition accuracy compared to the guided policy method. We
further look into optimizing the observation function to increase the total
collected reward of optimal policy. In AOR, the observation function is known
only approximately. We propose a gradient-based method update to this
approximate observation function to increase the total reward of any policy. We
show that by optimizing the observation function and retraining the supervised
LSTM network, the AOR performance on the test set improves significantly.Comment: IROS 201
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