1,103 research outputs found
A Bootstrap Lasso + Partial Ridge Method to Construct Confidence Intervals for Parameters in High-dimensional Sparse Linear Models
Constructing confidence intervals for the coefficients of high-dimensional
sparse linear models remains a challenge, mainly because of the complicated
limiting distributions of the widely used estimators, such as the lasso.
Several methods have been developed for constructing such intervals. Bootstrap
lasso+ols is notable for its technical simplicity, good interpretability, and
performance that is comparable with that of other more complicated methods.
However, bootstrap lasso+ols depends on the beta-min assumption, a theoretic
criterion that is often violated in practice. Thus, we introduce a new method,
called bootstrap lasso+partial ridge, to relax this assumption. Lasso+partial
ridge is a two-stage estimator. First, the lasso is used to select features.
Then, the partial ridge is used to refit the coefficients. Simulation results
show that bootstrap lasso+partial ridge outperforms bootstrap lasso+ols when
there exist small, but nonzero coefficients, a common situation that violates
the beta-min assumption. For such coefficients, the confidence intervals
constructed using bootstrap lasso+partial ridge have, on average, larger
coverage probabilities than those of bootstrap lasso+ols. Bootstrap
lasso+partial ridge also has, on average, shorter confidence interval
lengths than those of the de-sparsified lasso methods, regardless of whether
the linear models are misspecified. Additionally, we provide theoretical
guarantees for bootstrap lasso+partial ridge under appropriate conditions, and
implement it in the R package "HDCI.
Generating Features with Increased Crop-related Diversity for Few-Shot Object Detection
Two-stage object detectors generate object proposals and classify them to
detect objects in images. These proposals often do not contain the objects
perfectly but overlap with them in many possible ways, exhibiting great
variability in the difficulty levels of the proposals. Training a robust
classifier against this crop-related variability requires abundant training
data, which is not available in few-shot settings. To mitigate this issue, we
propose a novel variational autoencoder (VAE) based data generation model,
which is capable of generating data with increased crop-related diversity. The
main idea is to transform the latent space such latent codes with different
norms represent different crop-related variations. This allows us to generate
features with increased crop-related diversity in difficulty levels by simply
varying the latent norm. In particular, each latent code is rescaled such that
its norm linearly correlates with the IoU score of the input crop w.r.t. the
ground-truth box. Here the IoU score is a proxy that represents the difficulty
level of the crop. We train this VAE model on base classes conditioned on the
semantic code of each class and then use the trained model to generate features
for novel classes. In our experiments our generated features consistently
improve state-of-the-art few-shot object detection methods on the PASCAL VOC
and MS COCO datasets.Comment: Accepted to CVPR 2
Zero-Shot Object Counting with Language-Vision Models
Class-agnostic object counting aims to count object instances of an arbitrary
class at test time. It is challenging but also enables many potential
applications. Current methods require human-annotated exemplars as inputs which
are often unavailable for novel categories, especially for autonomous systems.
Thus, we propose zero-shot object counting (ZSC), a new setting where only the
class name is available during test time. This obviates the need for human
annotators and enables automated operation. To perform ZSC, we propose finding
a few object crops from the input image and use them as counting exemplars. The
goal is to identify patches containing the objects of interest while also being
visually representative for all instances in the image. To do this, we first
construct class prototypes using large language-vision models, including CLIP
and Stable Diffusion, to select the patches containing the target objects.
Furthermore, we propose a ranking model that estimates the counting error of
each patch to select the most suitable exemplars for counting. Experimental
results on a recent class-agnostic counting dataset, FSC-147, validate the
effectiveness of our method.Comment: Extended version of CVPR23 arXiv:2303.02001 . Currently under review
at T-PAM
Archives and fake news: Trust reconstruction in the "post-truth" era
The Purpose of this paper is to establish the mechanism of archives in the Trust Reconstruction in the 'post-truth' era. Through literature research , it is supposed to do some further analysis on the issues of archives, fake news, and trust. This paper may also take the external environment, technology, policy and other factors into account. Study found that fake news continuously erodes objective facts and makes us lose independent thinking, which is bad for our well-being. Archives can reconstruct trust though two ways, one is archival management, the other is Big Archival Data
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