208 research outputs found
Unsupervised Gaze-aware Contrastive Learning with Subject-specific Condition
Appearance-based gaze estimation has shown great promise in many applications
by using a single general-purpose camera as the input device. However, its
success is highly depending on the availability of large-scale well-annotated
gaze datasets, which are sparse and expensive to collect. To alleviate this
challenge we propose ConGaze, a contrastive learning-based framework that
leverages unlabeled facial images to learn generic gaze-aware representations
across subjects in an unsupervised way. Specifically, we introduce the
gaze-specific data augmentation to preserve the gaze-semantic features and
maintain the gaze consistency, which are proven to be crucial for effective
contrastive gaze representation learning. Moreover, we devise a novel
subject-conditional projection module that encourages a share feature extractor
to learn gaze-aware and generic representations. Our experiments on three
public gaze estimation datasets show that ConGaze outperforms existing
unsupervised learning solutions by 6.7% to 22.5%; and achieves 15.1% to 24.6%
improvement over its supervised learning-based counterpart in cross-dataset
evaluations
A Flexible Monopole Antenna with Dual-Notched Band Function for Ultrawideband Applications
We present a flexible ultrawideband (UWB) planar monopole antenna with dual-notched band characteristic printed on a polyimide substrate. The antenna is fed by a step coplanar waveguide (CPW) that provides smooth transitional impedance for improved matching. It operates from 2.76 to 10.6 GHz with return loss greater than 10 dB except for the notch band to reduce the interference with existing 3.5 GHz WiMAX band and 5.5 GHz WLAN band. With a combination of rectangular and circle patches in which the U-shaped slot is carved, the overall size of antenna is 30 mm × 20 mm. Moreover, a pair of arc-shaped stubs located at both sides of the feed line is utilized to create the notch band for WiMAX band. The results also show that the antenna has omnidirectional radiation pattern and smooth gain over the entire operational band
Efficient Multi-objective Evolutionary 3D Neural Architecture Search for COVID-19 Detection with Chest CT Scans
COVID-19 pandemic has spread globally for months. Due to its long incubation
period and high testing cost, there is no clue showing its spread speed is
slowing down, and hence a faster testing method is in dire need. This paper
proposes an efficient Evolutionary Multi-objective neural ARchitecture Search
(EMARS) framework, which can automatically search for 3D neural architectures
based on a well-designed search space for COVID-19 chest CT scan
classification. Within the framework, we use weight sharing strategy to
significantly improve the search efficiency and finish the search process in 8
hours. We also propose a new objective, namely potential, which is of benefit
to improve the search process's robustness. With the objectives of accuracy,
potential, and model size, we find a lightweight model (3.39 MB), which
outperforms three baseline human-designed models, i.e., ResNet3D101 (325.21
MB), DenseNet3D121 (43.06 MB), and MC3\_18 (43.84 MB). Besides, our
well-designed search space enables the class activation mapping algorithm to be
easily embedded into all searched models, which can provide the
interpretability for medical diagnosis by visualizing the judgment based on the
models to locate the lesion areas.Comment: Neural Architecture Search, Evolutionary Algorithm, COVID-19, C
ReLoop2: Building Self-Adaptive Recommendation Models via Responsive Error Compensation Loop
Industrial recommender systems face the challenge of operating in
non-stationary environments, where data distribution shifts arise from evolving
user behaviors over time. To tackle this challenge, a common approach is to
periodically re-train or incrementally update deployed deep models with newly
observed data, resulting in a continual training process. However, the
conventional learning paradigm of neural networks relies on iterative
gradient-based updates with a small learning rate, making it slow for large
recommendation models to adapt. In this paper, we introduce ReLoop2, a
self-correcting learning loop that facilitates fast model adaptation in online
recommender systems through responsive error compensation. Inspired by the
slow-fast complementary learning system observed in human brains, we propose an
error memory module that directly stores error samples from incoming data
streams. These stored samples are subsequently leveraged to compensate for
model prediction errors during testing, particularly under distribution shifts.
The error memory module is designed with fast access capabilities and undergoes
continual refreshing with newly observed data samples during the model serving
phase to support fast model adaptation. We evaluate the effectiveness of
ReLoop2 on three open benchmark datasets as well as a real-world production
dataset. The results demonstrate the potential of ReLoop2 in enhancing the
responsiveness and adaptiveness of recommender systems operating in
non-stationary environments.Comment: Accepted by KDD 2023. See the project page at
https://xpai.github.io/ReLoo
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