Search Results for author:
Found 16 papers, 9 papers with code
Unsolvable Problem Detection: Evaluating Trustworthiness of Vision Language Models
This paper introduces a novel and significant challenge for Vision Language Models (VLMs), termed Unsolvable Problem Detection (UPD).
Can Pre-trained Networks Detect Familiar Out-of-Distribution Data?
We consider that such data may significantly affect the performance of large pre-trained networks because the discriminability of these OOD data depends on the pre-training algorithm.
Out-of-Distribution Detection
Out of Distribution (OOD) Detection
CPR-Coach: Recognizing Composite Error Actions based on Single-class Training
For the first time, this paper constructs a vision-based system to complete error action recognition and skill assessment in CPR.
Open-Set Domain Adaptation with Visual-Language Foundation Models
Unsupervised domain adaptation (UDA) has proven to be very effective in transferring knowledge obtained from a source domain with labeled data to a target domain with unlabeled data.
LoCoOp: Few-Shot Out-of-Distribution Detection via Prompt Learning
CLIP's local features have a lot of ID-irrelevant nuisances (e. g., backgrounds), and by learning to push them away from the ID class text embeddings, we can remove the nuisances in the ID class text embeddings and enhance the separation between ID and OOD.
Out-of-Distribution Detection
Out of Distribution (OOD) Detection
Noisy Universal Domain Adaptation via Divergence Optimization for Visual Recognition
To transfer the knowledge learned from a labeled source domain to an unlabeled target domain, many studies have worked on universal domain adaptation (UniDA), where there is no constraint on the label sets of the source domain and target domain.
Zero-Shot In-Distribution Detection in Multi-Object Settings Using Vision-Language Foundation Models
First, images should be collected using only the name of the ID class without training on the ID data.
Rethinking Rotation in Self-Supervised Contrastive Learning: Adaptive Positive or Negative Data Augmentation
The semantics of an image can be rotation-invariant or rotation-variant, so whether the rotated image is treated as positive or negative should be determined based on the content of the image.
A Survey of Video-based Action Quality Assessment
Most of the existing work focuses on sports and medical care.
Noisy Annotation Refinement for Object Detection
In this study, we propose a new problem setting of training object detectors on datasets with entangled noises of annotations of class labels and bounding boxes.
Divergence Optimization for Noisy Universal Domain Adaptation
Hence, we consider a new realistic setting called Noisy UniDA, in which classifiers are trained with noisy labeled data from the source domain and unlabeled data with an unknown class distribution from the target domain.
The Gross-Llewellyn Smith sum rule up to ${\cal O}(α_s^4)$-order QCD corrections
In the paper, we analyze the properties of Gross-Llewellyn Smith (GLS) sum rule by using the $\mathcal{O}(\alpha_s^4)$-order QCD corrections with the help of principle of maximum conformality (PMC).
High Energy Physics - Phenomenology
The Aleatoric Uncertainty Estimation Using a Separate Formulation with Virtual Residuals
We propose a new optimization framework for aleatoric uncertainty estimation in regression problems.
Multi-Task Curriculum Framework for Open-Set Semi-Supervised Learning
Semi-supervised learning (SSL) has been proposed to leverage unlabeled data for training powerful models when only limited labeled data is available.
LungBRN: A Smart Digital Stethoscope for Detecting Respiratory Disease Using bi-ResNet Deep Learning Algorithm
Improving access to health care services for the medically under-served population is vital to ensure that critical illness can be addressed immediately.
Ranked #15 on
Audio Classification
on ICBHI Respiratory Sound Database
Unsupervised Out-of-Distribution Detection by Maximum Classifier Discrepancy
Unlike previous methods, we also utilize unlabeled data for unsupervised training and we use these unlabeled data to maximize the discrepancy between the decision boundaries of two classifiers to push OOD samples outside the manifold of the in-distribution (ID) samples, which enables us to detect OOD samples that are far from the support of the ID samples.
Out-of-Distribution Detection
Out of Distribution (OOD) Detection
