Joint Inference in Weakly-Annotated Image Datasets via Dense Correspondence
We present a principled framework for inferring pixel labels in weakly-annotated image datasets. Most previous, example-based approaches to computer vision rely on a large corpus of densely labeled images. However, for large, modern image datasets, such labels are expensive to obtain and are often u...
Main Authors: | Rubinstein, Michael, Liu, Ce, Freeman, William T. |
---|---|
Other Authors: | Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory |
Format: | Article |
Language: | English |
Published: |
Springer US
2017
|
Online Access: | http://hdl.handle.net/1721.1/106941 https://orcid.org/0000-0002-2231-7995 |
Similar Items
-
Annotation Propagation in Large Image Databases via Dense Image Correspondence
by: Rubinstein, Michael, et al.
Published: (2021) -
SIFT Flow: Dense Correspondence across Scenes and its Applications
by: Freeman, William T., et al.
Published: (2010) -
SIFT Flow: Dense Correspondence across Scenes and its Applications
by: Liu, Ce, et al.
Published: (2011) -
Weakly supervised salient object detection via image category annotation
by: Ruoqi Zhang, et al.
Published: (2023-12-01) -
Globe230k: A Benchmark Dense-Pixel Annotation Dataset for Global Land Cover Mapping
by: Qian Shi, et al.
Published: (2023-01-01)