Grounding spatial language for video search
By Stefanie Tellex, Thomas Kollar, George Shaw, Nicholas A. Roy, Deb K. Roy. 2010
21 citations recorded by the source corpus.
Subfields: Computer Vision and Pattern Recognition, Natural Language Processing, Multimodal Learning
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Computer Vision and Pattern Recognition
- Luc Van Gool
- Trevor Darrell
- Li Fei-Fei
- Dacheng Tao
- Xiaogang Wang
- Andrew Zisserman
- Jitendra Malik
- Bernt Schiele
In Natural Language Processing
- Mateusz Malinowski
- Mario Fritz
- Cynthia Matuszek
- Adam Santoro
- Peter W. Battaglia
- Mohit Shridhar
- David Hsu
- Nakul Gopalan
In Multimodal Learning
- Mateusz Malinowski
- Adam Santoro
- Peter W. Battaglia
- Mario Fritz
- David Hsu
- Nakul Gopalan
- Cynthia Matuszek
- Mohit Shridhar
Citations in the corpus
- A simple neural network module for relational reasoning (2017, background): “gh grounding language in spatial percepts has a long-standing tradition, the majority of previous research has focused on either rule-based spatial representations or hand-engineered spatial features [8, 10, 20, 21, 24, 29, 38, 39]. Although there are some attempts to learn spatial relations using s”
- Query Expansion Techniques for Information Retrieval: a Survey (2017, unknown): “Reference [254] expanded the query using the corpus of natural language description based on exact evaluation of system performance.”
- A Pooling Approach to Modelling Spatial Relations for Image Retrieval and Annotation (2014, background): “Grounding Spatial Relations Although research on grounding of spatial language has a long standing tradition, previous methods mostly focus on rule-based spatial representation [24, 16] or more recently on a set of hand-crafted spatial features with learnt weights [31, 9, 17, 10].”
- Interactive Visual Grounding of Referring Expressions for Human-Robot Interaction (2018, methodology): “Following earlier work [3, 25, 33], we focus on binary relations here, in Fig.”
- Robots That Use Language (2020, background): “…language is situated can take many forms; examples include sportscasts of simulated soccer games (10), linguistic descriptions of spatial elements in video clips (11), GUI interactions (12), descriptions of objects in the world (13), spatial relationships (14), and the meaning of instructions (15).”