mlsanityData to 7 Oct 2026

Bridging Gap between Image Pixels and Semantics via Supervision: A Survey

arXiv 2107.13757 ↗First posted 29 Jul 2021cs.CVcited by 4

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Abstract (arXiv)

The fact that there exists a gap between low-level features and semantic meanings of images, called the semantic gap, is known for decades. Resolution of the semantic gap is a long standing problem. The semantic gap problem is reviewed and a survey on recent efforts in bridging the gap is made in this work. Most importantly, we claim that the semantic gap is primarily bridged through supervised learning today. Experiences are drawn from two application domains to illustrate this point: 1) object detection and 2) metric learning for content-based image retrieval (CBIR). To begin with, this paper offers a historical retrospective on supervision, makes a gradual transition to the modern data-driven methodology and introduces commonly used datasets. Then, it summarizes various supervision methods to bridge the semantic gap in the context of object detection and metric learning.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 95 of its 209 references that are in the map. First posted on arXiv 29 Jul 2021.

Builds on

95 of 209 references.

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Citing papers per year: 2021: 1; 2022: 1; 2023: 0; 2024: 2.

4 papers.

  1. 2021CVAD: A generic medical anomaly detector based on Cascade VAEcited by 6
  2. 2022OSCARS: An Outlier-Sensitive Content-Based Radiography Retrieval Systemcited by 0
  3. 2024Accurately Classifying Out-Of-Distribution Data in Facial Recognitioncited by 0
  4. 2024StableSemantics: A Synthetic Language-Vision Dataset of Semantic Representations in Naturalistic Imagescited by 0