betekintés - Image Classification - # Low-shot image classification using pre-trained models

Leveraging Gaussian Processes to Assemble Pre-trained Vision and Language Models for Robust Low-shot Image Classification

Q: How can the proposed Bayesian framework be extended to incorporate other types of pre-trained models, such as those trained on multi-modal data or specialized for certain domains

The proposed Bayesian framework can be extended to incorporate other types of pre-trained models by adapting the mean function and kernel function to accommodate the unique characteristics of these models. For pre-trained models trained on multi-modal data, such as vision-language models, the mean function can be modified to incorporate both visual and textual information effectively. This can be achieved by leveraging the representations learned by the multi-modal model and combining them in a meaningful way within the Bayesian framework. The kernel function can also be adjusted to capture the relationships between different modalities in the data, allowing the model to make more informed predictions. Similarly, for pre-trained models specialized for certain domains, the mean function and kernel function can be tailored to leverage the domain-specific knowledge encoded in these models. By integrating the domain-specific features into the Bayesian framework, the model can better adapt to the intricacies of the target domain and improve its performance on tasks within that domain. Overall, by customizing the mean and kernel functions based on the characteristics of different pre-trained models, the Bayesian framework can be extended to incorporate a wide range of model types and domains effectively.

Q: What are the potential limitations of the label regression approach used in this work, and how could classification-based objectives be integrated while maintaining the benefits of the Bayesian formulation

One potential limitation of the label regression approach used in this work is its reliance on the assumption that the labels can be directly regressed from the input data. While this approach simplifies the modeling process and enables analytical inference, it may overlook the complex relationships between the input features and the target labels. In cases where the relationship is non-linear or involves intricate patterns, label regression may not capture the underlying structure effectively, leading to suboptimal performance. To address this limitation and incorporate classification-based objectives while maintaining the benefits of the Bayesian formulation, a hybrid approach can be adopted. This approach involves combining label regression with classification-based loss functions, such as cross-entropy loss, within the Bayesian framework. By incorporating classification objectives into the model training process, the model can learn more nuanced decision boundaries and improve its ability to generalize to unseen data. Additionally, techniques like semi-supervised learning or active learning can be integrated to leverage both labeled and unlabeled data effectively, enhancing the model's performance in low-shot scenarios.

Q: Given the success of the Bayesian approach in low-shot learning, how could similar principles be applied to other challenging computer vision tasks, such as few-shot object detection or segmentation

The success of the Bayesian approach in low-shot learning can be extended to other challenging computer vision tasks, such as few-shot object detection or segmentation, by leveraging similar principles tailored to the specific requirements of these tasks. In the context of few-shot object detection, Bayesian methods can be used to model the uncertainty associated with object localization and classification, enabling more robust and reliable detection in scenarios with limited training data. By incorporating prior knowledge from pre-trained object detection models and adapting the Bayesian framework to handle object-level annotations effectively, the model can learn to generalize better to new object categories with minimal supervision. Similarly, in few-shot segmentation tasks, Bayesian principles can be applied to model the uncertainty in pixel-wise predictions and incorporate prior knowledge from pre-trained segmentation models. By formulating the segmentation task as a probabilistic inference problem within the Bayesian framework, the model can capture the spatial dependencies between pixels and make informed decisions about object boundaries and shapes. Techniques like Monte Carlo dropout or variational inference can be employed to estimate uncertainty and improve the model's segmentation accuracy in low-shot scenarios. Overall, by extending Bayesian principles to these tasks, the models can achieve better generalization and performance in challenging computer vision applications.

Alapfogalmak

A Bayesian framework that effectively integrates prior knowledge from various pre-trained vision and language models to achieve superior low-shot image classification performance with well-calibrated uncertainty estimates.

Kivonat

The content presents a Bayesian approach to assemble pre-trained vision and language models, such as CLIP, DINO, and MoCo, for low-shot image classification. The key highlights are:

The method uses a Gaussian process (GP) regression framework to incorporate prior knowledge from multiple pre-trained models. The GP mean is specified by the zero-shot CLIP classifier, and the kernel is defined as an ensemble of deep kernels built upon various pre-trained models.
The Bayesian nature of the GP model enables analytical inference, straightforward uncertainty quantification, and principled hyperparameter tuning. The authors demonstrate that their method consistently outperforms competitive ensemble baselines in terms of predictive performance on standard low-shot image classification benchmarks.
The authors assess the robustness of their method and the quality of the yielded uncertainty estimates on out-of-distribution (OOD) datasets. They show that their method can effectively identify OOD samples and provide well-calibrated uncertainty estimates, which is crucial in high-risk domains.
Extensive ablation studies are conducted to understand the impact of different design choices, such as the GP mean and kernel, the choice of pre-trained models, and the optimization objective. The results demonstrate the importance of incorporating effective prior knowledge and the benefits of the Bayesian approach.

Összefoglaló testreszabása

Átírás mesterséges intelligenciával

Hivatkozások generálása

Forrás fordítása

Egy másik nyelvre

Gondolattérkép létrehozása

a forrásanyagból

Forrás megtekintése

arxiv.org

Statisztikák

The zero-shot CLIP classifier achieves an accuracy of 56.13% on the 16-shot ImageNet setting.
Our proposed method achieves an accuracy of 70.77% on the 16-shot ImageNet setting, outperforming the ensemble baselines.
The AUROC of our method to distinguish ImageNet and ImageNet-Sketch is 0.8545, indicating the superior quality of the uncertainty estimates.

Idézetek

"To bridge the gap, this work proposes a simple and effective probabilistic model ensemble framework based on Gaussian processes, which have previously demonstrated remarkable efficacy in processing small data."
"Such a modeling can address overfitting and result in calibrated post-data uncertainty arising from posterior inference."
"We achieve the integration of prior knowledge by specifying the mean function with CLIP and the kernel function with an ensemble of deep kernels built upon various pre-trained models."

Főbb Kivonatok

Bayesian Exploration of Pre-trained Models for Low-shot Image Classification

by Yibo Miao,Yu... : arxiv.org 04-02-2024

https://arxiv.org/pdf/2404.00312.pdf

Bayesian Exploration of Pre-trained Models for Low-shot Image Classification

Mélyebb kérdések

How can the proposed Bayesian framework be extended to incorporate other types of pre-trained models, such as those trained on multi-modal data or specialized for certain domains

The proposed Bayesian framework can be extended to incorporate other types of pre-trained models by adapting the mean function and kernel function to accommodate the unique characteristics of these models. For pre-trained models trained on multi-modal data, such as vision-language models, the mean function can be modified to incorporate both visual and textual information effectively. This can be achieved by leveraging the representations learned by the multi-modal model and combining them in a meaningful way within the Bayesian framework. The kernel function can also be adjusted to capture the relationships between different modalities in the data, allowing the model to make more informed predictions.
Similarly, for pre-trained models specialized for certain domains, the mean function and kernel function can be tailored to leverage the domain-specific knowledge encoded in these models. By integrating the domain-specific features into the Bayesian framework, the model can better adapt to the intricacies of the target domain and improve its performance on tasks within that domain. Overall, by customizing the mean and kernel functions based on the characteristics of different pre-trained models, the Bayesian framework can be extended to incorporate a wide range of model types and domains effectively.

What are the potential limitations of the label regression approach used in this work, and how could classification-based objectives be integrated while maintaining the benefits of the Bayesian formulation

One potential limitation of the label regression approach used in this work is its reliance on the assumption that the labels can be directly regressed from the input data. While this approach simplifies the modeling process and enables analytical inference, it may overlook the complex relationships between the input features and the target labels. In cases where the relationship is non-linear or involves intricate patterns, label regression may not capture the underlying structure effectively, leading to suboptimal performance.
To address this limitation and incorporate classification-based objectives while maintaining the benefits of the Bayesian formulation, a hybrid approach can be adopted. This approach involves combining label regression with classification-based loss functions, such as cross-entropy loss, within the Bayesian framework. By incorporating classification objectives into the model training process, the model can learn more nuanced decision boundaries and improve its ability to generalize to unseen data. Additionally, techniques like semi-supervised learning or active learning can be integrated to leverage both labeled and unlabeled data effectively, enhancing the model's performance in low-shot scenarios.

Given the success of the Bayesian approach in low-shot learning, how could similar principles be applied to other challenging computer vision tasks, such as few-shot object detection or segmentation

The success of the Bayesian approach in low-shot learning can be extended to other challenging computer vision tasks, such as few-shot object detection or segmentation, by leveraging similar principles tailored to the specific requirements of these tasks. In the context of few-shot object detection, Bayesian methods can be used to model the uncertainty associated with object localization and classification, enabling more robust and reliable detection in scenarios with limited training data. By incorporating prior knowledge from pre-trained object detection models and adapting the Bayesian framework to handle object-level annotations effectively, the model can learn to generalize better to new object categories with minimal supervision.
Similarly, in few-shot segmentation tasks, Bayesian principles can be applied to model the uncertainty in pixel-wise predictions and incorporate prior knowledge from pre-trained segmentation models. By formulating the segmentation task as a probabilistic inference problem within the Bayesian framework, the model can capture the spatial dependencies between pixels and make informed decisions about object boundaries and shapes. Techniques like Monte Carlo dropout or variational inference can be employed to estimate uncertainty and improve the model's segmentation accuracy in low-shot scenarios. Overall, by extending Bayesian principles to these tasks, the models can achieve better generalization and performance in challenging computer vision applications.