5 citations · 27 across the 22 of their papers we have counts for
6 papers · 1 filter
What can Computer Vision learn from Ranganathan?
Mayukh Bagchi, Fausto Giunchiglia
The Semantic Gap Problem (SGP) in Computer Vision (CV) arises from the misalignment between visual and lexical semantics leading to flawed CV dataset design and CV benchmarks. This…
A semantics-driven methodology for high-quality image annotation
Fausto Giunchiglia, Mayukh Bagchi, Xiaolei Diao
Recent work in Machine Learning and Computer Vision has highlighted the presence of various types of systematic flaws inside ground truth object recognition benchmark datasets. Our…
Incremental Image Labeling via Iterative Refinement
Fausto Giunchiglia, Xiaolei Diao, Mayukh Bagchi
Data quality is critical for multimedia tasks, while various types of systematic flaws are found in image benchmark datasets, as discussed in recent work. In particular, the existe…
Aligning Visual and Lexical Semantics
Fausto Giunchiglia, Mayukh Bagchi, Xiaolei Diao
We discuss two kinds of semantics relevant to Computer Vision (CV) systems - Visual Semantics and Lexical Semantics. While visual semantics focus on how humans build concepts when…
Visual Ground Truth Construction as Faceted Classification
Fausto Giunchiglia, Mayukh Bagchi, Xiaolei Diao
Recent work in Machine Learning and Computer Vision has provided evidence of systematic design flaws in the development of major object recognition benchmark datasets. One such exa…
Object Recognition as Classification via Visual Properties
Fausto Giunchiglia, Mayukh Bagchi
We base our work on the teleosemantic modelling of concepts as abilities implementing the distinct functions of recognition and classification. Accordingly, we model two types of c…