40 research outputs found
Improving Deep Regression with Ordinal Entropy
In computer vision, it is often observed that formulating regression problems
as a classification task often yields better performance. We investigate this
curious phenomenon and provide a derivation to show that classification, with
the cross-entropy loss, outperforms regression with a mean squared error loss
in its ability to learn high-entropy feature representations. Based on the
analysis, we propose an ordinal entropy loss to encourage higher-entropy
feature spaces while maintaining ordinal relationships to improve the
performance of regression tasks. Experiments on synthetic and real-world
regression tasks demonstrate the importance and benefits of increasing entropy
for regression.Comment: Accepted to ICLR 2023. Project page:
https://github.com/needylove/OrdinalEntrop
Early Detection of COVID-19 Hotspots Using Spatio-Temporal Data
Recently, the Centers for Disease Control and Prevention (CDC) has worked
with other federal agencies to identify counties with increasing coronavirus
disease 2019 (COVID-19) incidence (hotspots) and offers support to local health
departments to limit the spread of the disease. Understanding the
spatio-temporal dynamics of hotspot events is of great importance to support
policy decisions and prevent large-scale outbreaks. This paper presents a
spatio-temporal Bayesian framework for early detection of COVID-19 hotspots (at
the county level) in the United States. We assume both the observed number of
cases and hotspots depend on a class of latent random variables, which encode
the underlying spatio-temporal dynamics of the transmission of COVID-19. Such
latent variables follow a zero-mean Gaussian process, whose covariance is
specified by a non-stationary kernel function. The most salient feature of our
kernel function is that deep neural networks are introduced to enhance the
model's representative power while still enjoying the interpretability of the
kernel. We derive a sparse model and fit the model using a variational learning
strategy to circumvent the computational intractability for large data sets.
Our model demonstrates better interpretability and superior hotspot-detection
performance compared to other baseline methods
Learning to Generate Training Datasets for Robust Semantic Segmentation
Semantic segmentation techniques have shown significant progress in recent
years, but their robustness to real-world perturbations and data samples not
seen during training remains a challenge, particularly in safety-critical
applications. In this paper, we propose a novel approach to improve the
robustness of semantic segmentation techniques by leveraging the synergy
between label-to-image generators and image-to-label segmentation models.
Specifically, we design and train Robusta, a novel robust conditional
generative adversarial network to generate realistic and plausible perturbed or
outlier images that can be used to train reliable segmentation models. We
conduct in-depth studies of the proposed generative model, assess the
performance and robustness of the downstream segmentation network, and
demonstrate that our approach can significantly enhance the robustness of
semantic segmentation techniques in the face of real-world perturbations,
distribution shifts, and out-of-distribution samples. Our results suggest that
this approach could be valuable in safety-critical applications, where the
reliability of semantic segmentation techniques is of utmost importance and
comes with a limited computational budget in inference. We will release our
code shortly
1st Place Solution of Egocentric 3D Hand Pose Estimation Challenge 2023 Technical Report:A Concise Pipeline for Egocentric Hand Pose Reconstruction
This report introduce our work on Egocentric 3D Hand Pose Estimation
workshop. Using AssemblyHands, this challenge focuses on egocentric 3D hand
pose estimation from a single-view image. In the competition, we adopt ViT
based backbones and a simple regressor for 3D keypoints prediction, which
provides strong model baselines. We noticed that Hand-objects occlusions and
self-occlusions lead to performance degradation, thus proposed a non-model
method to merge multi-view results in the post-process stage. Moreover, We
utilized test time augmentation and model ensemble to make further improvement.
We also found that public dataset and rational preprocess are beneficial. Our
method achieved 12.21mm MPJPE on test dataset, achieve the first place in
Egocentric 3D Hand Pose Estimation challenge
Pressure-induced Superconductivity and Structure Phase Transition in SnAs-based Zintl Compound SrSn2As2
Layered SnAs-based Zintl compounds exhibit a distinctive electronic
structure, igniting extensive research efforts in areas of superconductivity,
topological insulators and quantum magnetism. In this paper, we systematically
investigate the crystal structures and electronic properties of the Zintl
compound SrSn2As2 under high-pressure. At approximately 20.8 GPa,
pressure-induced superconductivity is observed in SrSn2As2 with a
characteristic dome-like evolution of Tc. Theoretical calculations together
with high pressure synchrotron X-ray diffraction and Raman spectroscopy have
identified that SrSn2As2 undergoes a structural transformation from a trigonal
to a monoclinic structure. Beyond 28.3 GPa, the superconducting transition
temperature is suppressed due to a reduction of the density of state at the
Fermi level. The discovery of pressure-induced superconductivity, accompanied
by structural transitions in SrSn2As2, greatly expands the physical properties
of layered SnAs-based compounds and provides a new ground states upon
compression.Comment: 15 pages, 6 figures. arXiv admin note: text overlap with
arXiv:2307.1562
Pressure-induced Superconductivity in Zintl Topological Insulator SrIn2As2
The Zintl compound AIn2X2 (A = Ca, Sr, and X = P, As), as a theoretically
predicted new non-magnetic topological insulator, requires experiments to
understand their electronic structure and topological characteristics. In this
paper, we systematically investigate the crystal structures and electronic
properties of the Zintl compound SrIn2As2 under both ambient and high-pressure
conditions. Based on systematic angle-resolved photoemission spectroscopy
(ARPES) measurements, we observed the topological surface states on its (001)
surface as predicted by calculations, indicating that SrIn2As2 is a strong
topological insulator. Interestingly, application of pressure effectively tuned
the crystal structure and electronic properties of SrIn2As2. Superconductivity
is observed in SrIn2As2 for pressure where the temperature dependence of the
resistivity changes from a semiconducting-like behavior to that of a metal. The
observation of nontrivial topological states and pressure-induced
superconductivity in SrIn2As2 provides crucial insights into the relationship
between topology and superconductivity, as well as stimulates further studies
of superconductivity in topological materials.Comment: 15 pages,5 figure