247 research outputs found
Cooperative Multi-Objective Reinforcement Learning for Traffic Signal Control and Carbon Emission Reduction
Existing traffic signal control systems rely on oversimplified rule-based
methods, and even RL-based methods are often suboptimal and unstable. To
address this, we propose a cooperative multi-objective architecture called
Multi-Objective Multi-Agent Deep Deterministic Policy Gradient (MOMA-DDPG),
which estimates multiple reward terms for traffic signal control optimization
using age-decaying weights. Our approach involves two types of agents: one
focuses on optimizing local traffic at each intersection, while the other aims
to optimize global traffic throughput. We evaluate our method using real-world
traffic data collected from an Asian country's traffic cameras. Despite the
inclusion of a global agent, our solution remains decentralized as this agent
is no longer necessary during the inference stage. Our results demonstrate the
effectiveness of MOMA-DDPG, outperforming state-of-the-art methods across all
performance metrics. Additionally, our proposed system minimizes both waiting
time and carbon emissions. Notably, this paper is the first to link carbon
emissions and global agents in traffic signal control.Comment: arXiv admin note: substantial text overlap with arXiv:2205.1129
MixNet: Toward Accurate Detection of Challenging Scene Text in the Wild
Detecting small scene text instances in the wild is particularly challenging,
where the influence of irregular positions and nonideal lighting often leads to
detection errors. We present MixNet, a hybrid architecture that combines the
strengths of CNNs and Transformers, capable of accurately detecting small text
from challenging natural scenes, regardless of the orientations, styles, and
lighting conditions. MixNet incorporates two key modules: (1) the Feature
Shuffle Network (FSNet) to serve as the backbone and (2) the Central
Transformer Block (CTBlock) to exploit the 1D manifold constraint of the scene
text. We first introduce a novel feature shuffling strategy in FSNet to
facilitate the exchange of features across multiple scales, generating
high-resolution features superior to popular ResNet and HRNet. The FSNet
backbone has achieved significant improvements over many existing text
detection methods, including PAN, DB, and FAST. Then we design a complementary
CTBlock to leverage center line based features similar to the medial axis of
text regions and show that it can outperform contour-based approaches in
challenging cases when small scene texts appear closely. Extensive experimental
results show that MixNet, which mixes FSNet with CTBlock, achieves
state-of-the-art results on multiple scene text detection datasets
Parallel Residual Bi-Fusion Feature Pyramid Network for Accurate Single-Shot Object Detection
We propose the Parallel Residual Bi-Fusion Feature Pyramid Network (PRB-FPN)
for fast and accurate single-shot object detection. Feature Pyramid (FP) is
widely used in recent visual detection, however the top-down pathway of FP
cannot preserve accurate localization due to pooling shifting. The advantage of
FP is weaken as deeper backbones with more layers are used. To address this
issue, we propose a new parallel FP structure with bi-directional (top-down and
bottom-up) fusion and associated improvements to retain high-quality features
for accurate localization. Our method is particularly suitable for detecting
small objects. We provide the following design improvements: (1) A parallel
bifusion FP structure with a Bottom-up Fusion Module (BFM) to detect both small
and large objects at once with high accuracy. (2) A COncatenation and
RE-organization (CORE) module provides a bottom-up pathway for feature fusion,
which leads to the bi-directional fusion FP that can recover lost information
from lower-layer feature maps. (3) The CORE feature is further purified to
retain richer contextual information. Such purification is performed with CORE
in a few iterations in both top-down and bottom-up pathways. (4) The adding of
a residual design to CORE leads to a new Re-CORE module that enables easy
training and integration with a wide range of (deeper or lighter) backbones.
The proposed network achieves state-of-the-art performance on UAVDT17 and MS
COCO datasets.Comment: accepted by IEEE transactions on Image Processin
Emotion and Concentration Integrated System: Applied to the Detection and Analysis of Consumer Preference
With the expansion of consumer market, the appearance becomes an important issue when consumers make decisions under the situation of similar qualities and contents. Accordingly, to attract consumers, companies cost and take much attention on product appearance. Compared to using questionnaires individually, obtaining humansâ thoughts directly from their brains can accurately grasp the actual preference of consumers, which can provide effective and precious decisions for companies. \ In this study, consumersâ brainwaves which are related to concentration and emotion are extracted by wearing a portable and wireless Electroencephalography (EEG) device. The extracted EEG data are then trained by using perceptron learning algorithm (PLA) to make the judgments of concentration and emotion work well with each subject. They are then applied to the detection and analysis of consumer preference. Finally, the questionnaires are also performed and used as the reference on training process. They are integrated with brainwaves data to create one prediction model which can improve the accuracy significantly. The Partial Least Squares is used to compare the correlation between different factors in the model, to ensure the test can accurately meet consumersâ thoughts
Orthogonal magnetization and symmetry breaking in pyrochlore iridate Eu2Ir2O7
Electrons in the pyrochlore iridates experience a large interaction energy in addition to a strong spin-orbit interaction. Both features make the iridates promising for realizing novel states such as the topological Mott insulator.The pyrochlore iridate EuâIrâOâ shows a metal-insulator transition at T[subscript N] 1/4 120 K below which a magnetically ordered state develops. Using torque magnetometry, we uncover an unusual magnetic response. A magnetic field H applied in its a-b plane produces a nonlinear magnetization Mâ„ orthogonal to the plane. Mâ„ displays a d-wave field-angle pattern consistent with octupolar order, with a handedness dictated by field cooling, leading to symmetry breaking of the chirality. A surprise is that the lobe orientation of the d-wave pattern is sensitive to the direction of the field when the sample is field-cooled below T[subscript N], suggestive of an additional order parameter already present at 300 K.National Science Foundation (U.S.) (Grant DMR 1420541)Gordon and Betty Moore Foundation (Grant GBMF4539)United States. Department of Energy (Grant DE-SC0010526
SMILEtrack: SiMIlarity LEarning for Occlusion-Aware Multiple Object Tracking
Despite recent progress in Multiple Object Tracking (MOT), several obstacles
such as occlusions, similar objects, and complex scenes remain an open
challenge. Meanwhile, a systematic study of the cost-performance tradeoff for
the popular tracking-by-detection paradigm is still lacking. This paper
introduces SMILEtrack, an innovative object tracker that effectively addresses
these challenges by integrating an efficient object detector with a Siamese
network-based Similarity Learning Module (SLM). The technical contributions of
SMILETrack are twofold. First, we propose an SLM that calculates the appearance
similarity between two objects, overcoming the limitations of feature
descriptors in Separate Detection and Embedding (SDE) models. The SLM
incorporates a Patch Self-Attention (PSA) block inspired by the vision
Transformer, which generates reliable features for accurate similarity
matching. Second, we develop a Similarity Matching Cascade (SMC) module with a
novel GATE function for robust object matching across consecutive video frames,
further enhancing MOT performance. Together, these innovations help SMILETrack
achieve an improved trade-off between the cost ({\em e.g.}, running speed) and
performance (e.g., tracking accuracy) over several existing state-of-the-art
benchmarks, including the popular BYTETrack method. SMILETrack outperforms
BYTETrack by 0.4-0.8 MOTA and 2.1-2.2 HOTA points on MOT17 and MOT20 datasets.
Code is available at https://github.com/pingyang1117/SMILEtrack_Officia
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