487 research outputs found
Computed Web Learning Software Design with a Medical Psychological Perspective: Depression as an Example and Economic Analysis
We have tried to use computer technology in teaching and designing the necessary knowledge points for the diagnosis, treatment, and prevention of depression. We have also used computer platforms to elucidate this model as an economics product and carry out the necessary investigation and study of the market prospects, and we have proposed innovative points in solving the problem based on basic knowledge in medical psychology, and we have reported the results in conjunction with the results of the study
Robust Transductive Few-shot Learning via Joint Message Passing and Prototype-based Soft-label Propagation
Few-shot learning (FSL) aims to develop a learning model with the ability to
generalize to new classes using a few support samples. For transductive FSL
tasks, prototype learning and label propagation methods are commonly employed.
Prototype methods generally first learn the representative prototypes from the
support set and then determine the labels of queries based on the metric
between query samples and prototypes. Label propagation methods try to
propagate the labels of support samples on the constructed graph encoding the
relationships between both support and query samples. This paper aims to
integrate these two principles together and develop an efficient and robust
transductive FSL approach, termed Prototype-based Soft-label Propagation
(PSLP). Specifically, we first estimate the soft-label presentation for each
query sample by leveraging prototypes. Then, we conduct soft-label propagation
on our learned query-support graph. Both steps are conducted progressively to
boost their respective performance. Moreover, to learn effective prototypes for
soft-label estimation as well as the desirable query-support graph for
soft-label propagation, we design a new joint message passing scheme to learn
sample presentation and relational graph jointly. Our PSLP method is
parameter-free and can be implemented very efficiently. On four popular
datasets, our method achieves competitive results on both balanced and
imbalanced settings compared to the state-of-the-art methods. The code will be
released upon acceptance
Unsupervised Low Light Image Enhancement Using SNR-Aware Swin Transformer
Image captured under low-light conditions presents unpleasing artifacts,
which debilitate the performance of feature extraction for many upstream visual
tasks. Low-light image enhancement aims at improving brightness and contrast,
and further reducing noise that corrupts the visual quality. Recently, many
image restoration methods based on Swin Transformer have been proposed and
achieve impressive performance. However, On one hand, trivially employing Swin
Transformer for low-light image enhancement would expose some artifacts,
including over-exposure, brightness imbalance and noise corruption, etc. On the
other hand, it is impractical to capture image pairs of low-light images and
corresponding ground-truth, i.e. well-exposed image in same visual scene. In
this paper, we propose a dual-branch network based on Swin Transformer, guided
by a signal-to-noise ratio prior map which provides the spatial-varying
information for low-light image enhancement. Moreover, we leverage unsupervised
learning to construct the optimization objective based on Retinex model, to
guide the training of proposed network. Experimental results demonstrate that
the proposed model is competitive with the baseline models
Two-stream Multi-level Dynamic Point Transformer for Two-person Interaction Recognition
As a fundamental aspect of human life, two-person interactions contain
meaningful information about people's activities, relationships, and social
settings. Human action recognition serves as the foundation for many smart
applications, with a strong focus on personal privacy. However, recognizing
two-person interactions poses more challenges due to increased body occlusion
and overlap compared to single-person actions. In this paper, we propose a
point cloud-based network named Two-stream Multi-level Dynamic Point
Transformer for two-person interaction recognition. Our model addresses the
challenge of recognizing two-person interactions by incorporating local-region
spatial information, appearance information, and motion information. To achieve
this, we introduce a designed frame selection method named Interval Frame
Sampling (IFS), which efficiently samples frames from videos, capturing more
discriminative information in a relatively short processing time. Subsequently,
a frame features learning module and a two-stream multi-level feature
aggregation module extract global and partial features from the sampled frames,
effectively representing the local-region spatial information, appearance
information, and motion information related to the interactions. Finally, we
apply a transformer to perform self-attention on the learned features for the
final classification. Extensive experiments are conducted on two large-scale
datasets, the interaction subsets of NTU RGB+D 60 and NTU RGB+D 120. The
results show that our network outperforms state-of-the-art approaches across
all standard evaluation settings
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