Statistical Learning
Progressive Exploration-Conformal Learning for Sparsely Annotated Object Detection in Aerial Images
The ability to detect aerial objects with limited annotation is pivotal to the development of real-world aerial intelligence systems. In this work, we focus on a demanding but practical sparsely annotated object detection (SAOD) in aerial images, which encompasses a wider variety of aerial scenes with the same number of annotated objects.
Reshuffling Resampling Splits Can Improve Generalization of Hyperparameter Optimization
Hyperparameter optimization is crucial for obtaining peak performance of machine learning models.
Overleaf Example
Prediction is a fundamental capability of all living organisms, and has been proposed as an objective for learning sensory representations.
DenoiseRep: Denoising Model for Representation Learning
The denoising model has been proven a powerful generative model but has little exploration of discriminative tasks. Representation learning is important in discriminative tasks, which is defined as "learning representations (or features) of
Harnessing small projectors and multiple views for efficient vision pretraining
We show how this idealized loss can be reformulated to a functionally equivalent loss that is more efficient to compute.
Interpreting and Analysing CLIP's Zero-Shot Image Classification via Mutual Knowledge
We explore those different aspects in relation to mutual knowledge, and analyze zero-shot predictions.