8,102 research outputs found
Multi-shot Pedestrian Re-identification via Sequential Decision Making
Multi-shot pedestrian re-identification problem is at the core of
surveillance video analysis. It matches two tracks of pedestrians from
different cameras. In contrary to existing works that aggregate single frames
features by time series model such as recurrent neural network, in this paper,
we propose an interpretable reinforcement learning based approach to this
problem. Particularly, we train an agent to verify a pair of images at each
time. The agent could choose to output the result (same or different) or
request another pair of images to verify (unsure). By this way, our model
implicitly learns the difficulty of image pairs, and postpone the decision when
the model does not accumulate enough evidence. Moreover, by adjusting the
reward for unsure action, we can easily trade off between speed and accuracy.
In three open benchmarks, our method are competitive with the state-of-the-art
methods while only using 3% to 6% images. These promising results demonstrate
that our method is favorable in both efficiency and performance
Recent Advances in Deep Learning Techniques for Face Recognition
In recent years, researchers have proposed many deep learning (DL) methods
for various tasks, and particularly face recognition (FR) made an enormous leap
using these techniques. Deep FR systems benefit from the hierarchical
architecture of the DL methods to learn discriminative face representation.
Therefore, DL techniques significantly improve state-of-the-art performance on
FR systems and encourage diverse and efficient real-world applications. In this
paper, we present a comprehensive analysis of various FR systems that leverage
the different types of DL techniques, and for the study, we summarize 168
recent contributions from this area. We discuss the papers related to different
algorithms, architectures, loss functions, activation functions, datasets,
challenges, improvement ideas, current and future trends of DL-based FR
systems. We provide a detailed discussion of various DL methods to understand
the current state-of-the-art, and then we discuss various activation and loss
functions for the methods. Additionally, we summarize different datasets used
widely for FR tasks and discuss challenges related to illumination, expression,
pose variations, and occlusion. Finally, we discuss improvement ideas, current
and future trends of FR tasks.Comment: 32 pages and citation: M. T. H. Fuad et al., "Recent Advances in Deep
Learning Techniques for Face Recognition," in IEEE Access, vol. 9, pp.
99112-99142, 2021, doi: 10.1109/ACCESS.2021.309613
Dependency-aware Attention Control for Unconstrained Face Recognition with Image Sets
This paper targets the problem of image set-based face verification and
identification. Unlike traditional single media (an image or video) setting, we
encounter a set of heterogeneous contents containing orderless images and
videos. The importance of each image is usually considered either equal or
based on their independent quality assessment. How to model the relationship of
orderless images within a set remains a challenge. We address this problem by
formulating it as a Markov Decision Process (MDP) in the latent space.
Specifically, we first present a dependency-aware attention control (DAC)
network, which resorts to actor-critic reinforcement learning for sequential
attention decision of each image embedding to fully exploit the rich
correlation cues among the unordered images. Moreover, we introduce its
sample-efficient variant with off-policy experience replay to speed up the
learning process. The pose-guided representation scheme can further boost the
performance at the extremes of the pose variation.Comment: Fixed the unreadable code in CVF version. arXiv admin note: text
overlap with arXiv:1707.00130 by other author
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