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TeD-Loc: Text distillation for weakly supervised object localization
Shakeeb Murtaza
, Soufiane Belharbi
, Alexis Guichemerre
,
Marco Pedersoli
,
Eric Granger
École de technologie supérieure
Systems Engineering Department
LIVIA - Imaging, Vision and Artificial Intelligence Laboratory
itechsanté - Research Institute for Innovation in Health Technologies
École de technologie supérieure
Research output
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Contribution to journal
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Journal Article
›
peer-review
Overview
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Keyphrases
Bounding Box Annotation
20%
Class Activation Map
20%
Class Token
20%
Classification Module
20%
Contrastive Alignment
20%
Denoising
20%
Distillation
100%
Efficient Inference
20%
Foreground-background
20%
Global Classes
20%
Global Text
20%
Histopathology
20%
ILSVRC
20%
Joint Classification
20%
Joint Localization
20%
Knowledge Distillation
20%
Learning Strategies
20%
Local Patch
20%
Localization Method
20%
Localization Model
20%
Localization Rate
20%
Object Class
20%
Object Region
20%
Orthogonalization
20%
Patch Embedding
60%
Patch Level
40%
Prompt Learning
20%
Rich Semantics
20%
Semantic Prior
20%
Similarity Classes
20%
Single Model
20%
Spatial Regions
20%
Text Embedding
40%
Token Embeddings
20%
Weakly Supervised Object Localization
100%
Computer Science
Annotation
100%
Class Activation Mapping
100%
de-noising
100%
localization algorithm
100%
Orthogonalization
100%
Prompt Learning
100%
Spatial Extent
100%