Search Results for author:
Found 6 papers, 4 papers with code
Enhanced Performance of Pre-Trained Networks by Matched Augmentation Distributions
Specifically, we demonstrate that running inference on the center crop of an image is not always the best as important discriminatory information may be cropped-off.
Resource Efficient Mountainous Skyline Extraction using Shallow Learning
We present a novel mountainous skyline detection approach where we adapt a shallow learning approach to learn a set of filters to discriminate between edges belonging to sky-mountain boundary and others coming from different regions.
Self-Supervised Features Improve Open-World Learning
This paper identifies the flaws in existing open-world learning approaches and attempts to provide a complete picture in the form of \textbf{True Open-World Learning}.
A Review of Open-World Learning and Steps Toward Open-World Learning Without Labels
Open-world learning is related to but also distinct from a multitude of other learning problems and this paper briefly analyzes the key differences between a wide range of problems including incremental learning, generalized novelty discovery, and generalized zero-shot learning.
Automatic Open-World Reliability Assessment
However, during operations, we cannot directly assess accuracy as there are no labels.
Comparison of Semantic Segmentation Approaches for Horizon/Sky Line Detection
Each of the method is tested on an extensive test set (about 3K images) covering various challenging geographical, weather, illumination and seasonal conditions.
