144 research outputs found
Time Series as Images: Vision Transformer for Irregularly Sampled Time Series
Irregularly sampled time series are increasingly prevalent, particularly in
medical domains. While various specialized methods have been developed to
handle these irregularities, effectively modeling their complex dynamics and
pronounced sparsity remains a challenge. This paper introduces a novel
perspective by converting irregularly sampled time series into line graph
images, then utilizing powerful pre-trained vision transformers for time series
classification in the same way as image classification. This method not only
largely simplifies specialized algorithm designs but also presents the
potential to serve as a universal framework for time series modeling.
Remarkably, despite its simplicity, our approach outperforms state-of-the-art
specialized algorithms on several popular healthcare and human activity
datasets. Especially in the rigorous leave-sensors-out setting where a portion
of variables is omitted during testing, our method exhibits strong robustness
against varying degrees of missing observations, achieving an impressive
improvement of 42.8% in absolute F1 score points over leading specialized
baselines even with half the variables masked. Code and data are available at
https://github.com/Leezekun/ViTSTComment: Accepted to NeurIPS2023. Code and data are available at:
https://github.com/Leezekun/ViTS
Hyp-UML: Hyperbolic Image Retrieval with Uncertainty-aware Metric Learning
Metric learning plays a critical role in training image retrieval and
classification. It is also a key algorithm in representation learning, e.g.,
for feature learning and its alignment in metric space. Hyperbolic embedding
has been recently developed. Compared to the conventional Euclidean embedding
in most of the previously developed models, Hyperbolic embedding can be more
effective in representing the hierarchical data structure. Second, uncertainty
estimation/measurement is a long-lasting challenge in artificial intelligence.
Successful uncertainty estimation can improve a machine learning model's
performance, robustness, and security. In Hyperbolic space, uncertainty
measurement is at least with equivalent, if not more, critical importance. In
this paper, we develop a Hyperbolic image embedding with uncertainty-aware
metric learning for image retrieval. We call our method Hyp-UML: Hyperbolic
Uncertainty-aware Metric Learning. Our contribution are threefold: we propose
an image embedding algorithm based on Hyperbolic space, with their
corresponding uncertainty value; we propose two types of uncertainty-aware
metric learning, for the popular Contrastive learning and conventional
margin-based metric learning, respectively. We perform extensive experimental
validations to prove that the proposed algorithm can achieve state-of-the-art
results among related methods. The comprehensive ablation study validates the
effectiveness of each component of the proposed algorithm
Vehicle Re-identification in Still Images: Application of Semi-supervised Learning and Re-ranking
Vehicle re-identification (re-ID), namely, finding exactly the same vehicle from a large number of vehicle images, remains a great challenge in computer vision. Most existing vehicle re-ID approaches follow a fully supervised learning methodology, in which sufficient labeled training data is required. However, this limits their scalability to realistic applications, due to the high cost of data labeling. In this paper, we adopted a Generative Adversarial Network (GAN) to generate unlabeled samples and enlarge the training set. A semi supervised learning scheme with the Convolutional Neural Networks (CNN) was proposed accordingly, which assigns a uniform label distribution to the unlabeled images to regularize the supervised model and improve the performance of the vehicle re-ID system. Besides, an improved re-ranking method based on the Jaccard distance and k-reciprocal nearest neighbors is proposed to optimize the initial rank list. Extensive experiments over the benchmark datasets VeR1-776, VehicleID and VehicleReID have demonstrated that the proposed method outperforms the state-of-the-art approaches for vehicle re-ID
Comparaison de la morphologie du pied entre les enfants chinois et mongoliens
Knowledge of foot morphology is fundamental to optimize children’s footwear design. The aim of this study is to compare the foot morphology of Chinese and Mongolian children from 7 to 14 years old. Relative data of 339 Mongolian children and another matched 379 Chinese children were collected using 3D foot scanner. The findings of this study are as follows: i) the absolute foot length of Chinese children is significantly greater than that of Mongolian children of the same age; ii) Mongolian children show significantly greater heel width, toe thickness, lateral malleolus height, instep height and ball girth compared to Chinese children of the same age. The foot width of Chinese children is significantly greater than that of Mongolian children of the same age; iii) Chinese children have a higher risk of hallux valgus than Mongolian children of both sexes. Small variations in foot morphology discussed in this paper could be useful when considering the shoes design for Mongolian and Chinese children. © 2020 by the author(s)
Instruction-following Evaluation through Verbalizer Manipulation
While instruction-tuned models have shown remarkable success in various
natural language processing tasks, accurately evaluating their ability to
follow instructions remains challenging. Existing benchmarks primarily focus on
common instructions that align well with what the model learned during
training. However, proficiency in responding to these instructions does not
necessarily imply strong ability in instruction following. In this paper, we
propose a novel instruction-following evaluation protocol called verbalizer
manipulation. It instructs the model to verbalize the task label with words
aligning with model priors to different extents, adopting verbalizers from
highly aligned (e.g., outputting ``postive'' for positive sentiment), to
minimally aligned (e.g., outputting ``negative'' for positive sentiment).
Verbalizer manipulation can be seamlessly integrated with any classification
benchmark to examine the model's reliance on priors and its ability to override
them to accurately follow the instructions. We conduct a comprehensive
evaluation of four major model families across nine datasets, employing twelve
sets of verbalizers for each of them. We observe that the instruction-following
abilities of models, across different families and scales, are significantly
distinguished by their performance on less natural verbalizers. Even the
strongest GPT-4 model struggles to perform better than random guessing on the
most challenging verbalizer, emphasizing the need for continued advancements to
improve their instruction-following abilities
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