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Found 13 papers, 5 papers with code
Waffling around for Performance: Visual Classification with Random Words and Broad Concepts
The visual classification performance of vision-language models such as CLIP has been shown to benefit from additional semantic knowledge from large language models (LLMs) such as GPT-3.
Exposing and Mitigating Spurious Correlations for Cross-Modal Retrieval
In this work, we introduce ODmAP@k, an object decorrelation metric that measures a model's robustness to spurious correlations in the training data.
Bridging the Gap between Model Explanations in Partially Annotated Multi-label Classification
Based on these findings, we propose to boost the attribution scores of the model trained with partial labels to make its explanation resemble that of the model trained with full labels.
Likelihood Annealing: Fast Calibrated Uncertainty for Regression
Recent advances in deep learning have shown that uncertainty estimation is becoming increasingly important in applications such as medical imaging, natural language processing, and autonomous systems.
Large Loss Matters in Weakly Supervised Multi-Label Classification
In this work, we first regard unobserved labels as negative labels, casting the WSML task into noisy multi-label classification.
Variational Perturbations for Visual Feature Attribution
Explaining a complex black-box system in a post-hoc manner is important to understand its predictions.
Beyond Examples: Constructing Explanation Space for Explaining Prototypes
As deep learning has been successfully deployed in diverse applications, there is ever increasing need for explaining its decision.
Distribution-Driven Disjoint Prediction Intervals for Deep Learning
To address the issue, we propose a novel method that generates a union of disjoint PIs.
Keep CALM and Improve Visual Feature Attribution
The class activation mapping, or CAM, has been the cornerstone of feature attribution methods for multiple vision tasks.
Variational saliency maps for explaining model's behavior
Saliency maps have been widely used to explain the behavior of an image classifier.
REST: Performance Improvement of a Black Box Model via RL-based Spatial Transformation
Our work aims to improve the robustness by adding a REST module in front of any black boxes and training only the REST module without retraining the original black box model in an end-to-end manner, i. e. we try to convert the real-world data into training distribution which the performance of the black-box model is best suited for.
Exploring linearity of deep neural network trained QSM: QSMnet+
To overcome this limitation, a data augmentation method is proposed to generalize the network for a wider range of susceptibility.
Image and Video Processing
Sampling-based Bayesian Inference with gradient uncertainty
In this paper, we show that predictive uncertainty can be efficiently estimated when we incorporate the concept of gradients uncertainty into posterior sampling.
