103 research outputs found
Freeway Traffic Density and On-Ramp Queue Control via ILC Approach
A new queue length information fused iterative learning control approach (QLIF-ILC) is presented for freeway traffic ramp metering to achieve a better performance by utilizing the error information of the on-ramp queue length. The QLIF-ILC consists of two parts, where the iterative feedforward part updates the control input signal by learning from the past control data in previous trials, and the current feedback part utilizes the tracking error of the current learning iteration to stabilize the controlled plant. These two parts are combined in a complementary manner to enhance the robustness of the proposed QLIF-ILC. A systematic approach is developed to analyze the convergence and robustness of the proposed learning scheme. The simulation results are further given to demonstrate the effectiveness of the proposed QLIF-ILC
Research on Water Pollution Control Based on STM32 Intelligent Vehicle
In order to solve the high cost and low efficiency of different degrees of pollution control of natural water resources in China at this stage, photocatalytic water purification technology is adopted to reduce the cost of water pollution treatment and improve the treatment efficiency, and an intelligent vehicle equipped with photocatalytic materials is proposed, which is equipped with industrial cameras, communication positioning modules and sensors, and realizes dynamic planning of navigation routes by improving ant colony algorithms, computer vision recognition, ultrasonic obstacle avoidance, and realizes photocatalytic fixed-point purification. Predict advanced photoelectric catalytic performance based on density functional theory and machine learning, solve the problem of BiVO4 photo corrosion and instability, and achieve efficient water purification at low cost
Panoptic Scene Graph Generation with Semantics-prototype Learning
Panoptic Scene Graph Generation (PSG) parses objects and predicts their
relationships (predicate) to connect human language and visual scenes. However,
different language preferences of annotators and semantic overlaps between
predicates lead to biased predicate annotations in the dataset, i.e. different
predicates for same object pairs. Biased predicate annotations make PSG models
struggle in constructing a clear decision plane among predicates, which greatly
hinders the real application of PSG models. To address the intrinsic bias
above, we propose a novel framework named ADTrans to adaptively transfer biased
predicate annotations to informative and unified ones. To promise consistency
and accuracy during the transfer process, we propose to measure the invariance
of representations in each predicate class, and learn unbiased prototypes of
predicates with different intensities. Meanwhile, we continuously measure the
distribution changes between each presentation and its prototype, and
constantly screen potential biased data. Finally, with the unbiased
predicate-prototype representation embedding space, biased annotations are
easily identified. Experiments show that ADTrans significantly improves the
performance of benchmark models, achieving a new state-of-the-art performance,
and shows great generalization and effectiveness on multiple datasets
Enhancing Fairness of Visual Attribute Predictors
The performance of deep neural networks for image recognition tasks such as
predicting a smiling face is known to degrade with under-represented classes of
sensitive attributes. We address this problem by introducing fairness-aware
regularization losses based on batch estimates of Demographic Parity, Equalized
Odds, and a novel Intersection-over-Union measure. The experiments performed on
facial and medical images from CelebA, UTKFace, and the SIIM-ISIC melanoma
classification challenge show the effectiveness of our proposed fairness losses
for bias mitigation as they improve model fairness while maintaining high
classification performance. To the best of our knowledge, our work is the first
attempt to incorporate these types of losses in an end-to-end training scheme
for mitigating biases of visual attribute predictors. Our code is available at
https://github.com/nish03/FVAP.Comment: Camera Ready, ACCV 202
Revisiting the Domain Shift and Sample Uncertainty in Multi-source Active Domain Transfer
Active Domain Adaptation (ADA) aims to maximally boost model adaptation in a
new target domain by actively selecting a limited number of target data to
annotate.This setting neglects the more practical scenario where training data
are collected from multiple sources. This motivates us to target a new and
challenging setting of knowledge transfer that extends ADA from a single source
domain to multiple source domains, termed Multi-source Active Domain Adaptation
(MADA). Not surprisingly, we find that most traditional ADA methods cannot work
directly in such a setting, mainly due to the excessive domain gap introduced
by all the source domains and thus their uncertainty-aware sample selection can
easily become miscalibrated under the multi-domain shifts. Considering this, we
propose a Dynamic integrated uncertainty valuation framework(Detective) that
comprehensively consider the domain shift between multi-source domains and
target domain to detect the informative target samples. Specifically, the
leverages a dynamic Domain Adaptation(DA) model that learns how to adapt the
model's parameters to fit the union of multi-source domains. This enables an
approximate single-source domain modeling by the dynamic model. We then
comprehensively measure both domain uncertainty and predictive uncertainty in
the target domain to detect informative target samples using evidential deep
learning, thereby mitigating uncertainty miscalibration. Furthermore, we
introduce a contextual diversity-aware calculator to enhance the diversity of
the selected samples. Experiments demonstrate that our solution outperforms
existing methods by a considerable margin on three domain adaptation
benchmarks.Comment: arXiv admin note: text overlap with arXiv:2302.13824 by other author
IDEAL: Toward High-efficiency Device-Cloud Collaborative and Dynamic Recommendation System
Recommendation systems have shown great potential to solve the information
explosion problem and enhance user experience in various online applications,
which recently present two emerging trends: (i) Collaboration: single-sided
model trained on-cloud (separate learning) to the device-cloud collaborative
recommendation (collaborative learning). (ii) Real-time Dynamic: the network
parameters are the same across all the instances (static model) to adaptive
network parameters generation conditioned on the real-time instances (dynamic
model). The aforementioned two trends enable the device-cloud collaborative and
dynamic recommendation, which deeply exploits the recommendation pattern among
cloud-device data and efficiently characterizes different instances with
different underlying distributions based on the cost of frequent device-cloud
communication. Despite promising, we argue that most of the communications are
unnecessary to request the new parameters of the recommendation system on the
cloud since the on-device data distribution are not always changing. To
alleviate this issue, we designed a Intelligent DEvice-Cloud PArameter Request
ModeL (IDEAL) that can be deployed on the device to calculate the request
revenue with low resource consumption, so as to ensure the adaptive
device-cloud communication with high revenue. We envision a new device
intelligence learning task to implement IDEAL by detecting the data
out-of-domain. Moreover, we map the user's real-time behavior to a normal
distribution, the uncertainty is calculated by the multi-sampling outputs to
measure the generalization ability of the device model to the current user
behavior. Our experimental study demonstrates IDEAL's effectiveness and
generalizability on four public benchmarks, which yield a higher efficient
device-cloud collaborative and dynamic recommendation paradigm
Gradient-Regulated Meta-Prompt Learning for Generalizable Vision-Language Models
Prompt tuning, a recently emerging paradigm, enables the powerful
vision-language pre-training models to adapt to downstream tasks in a parameter
-- and data -- efficient way, by learning the ``soft prompts'' to condition
frozen pre-training models. Though effective, it is particularly problematic in
the few-shot scenario, where prompt tuning performance is sensitive to the
initialization and requires a time-consuming process to find a good
initialization, thus restricting the fast adaptation ability of the
pre-training models. In addition, prompt tuning could undermine the
generalizability of the pre-training models, because the learnable prompt
tokens are easy to overfit to the limited training samples. To address these
issues, we introduce a novel Gradient-RegulAted Meta-prompt learning (GRAM)
framework that jointly meta-learns an efficient soft prompt initialization for
better adaptation and a lightweight gradient regulating function for strong
cross-domain generalizability in a meta-learning paradigm using only the
unlabeled image-text pre-training data. Rather than designing a specific prompt
tuning method, our GRAM can be easily incorporated into various prompt tuning
methods in a model-agnostic way, and comprehensive experiments show that GRAM
brings about consistent improvement for them in several settings (i.e.,
few-shot learning, cross-domain generalization, cross-dataset generalization,
etc.) over 11 datasets. Further, experiments show that GRAM enables the
orthogonal methods of textual and visual prompt tuning to work in a
mutually-enhanced way, offering better generalizability beyond the uni-modal
prompt tuning methods.Comment: Accepted by ICCV 202
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