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Found 2 papers, 2 papers with code
Learning To Segment Every Referring Object Point by Point
Extensive experiments show that our model achieves 52. 06% in terms of accuracy (versus 58. 93% in fully supervised setting) on RefCOCO+@testA, when only using 1% of the mask annotations.
SiRi: A Simple Selective Retraining Mechanism for Transformer-based Visual Grounding
Particularly, SiRi conveys a significant principle to the research of visual grounding, i. e., a better initialized vision-language encoder would help the model converge to a better local minimum, advancing the performance accordingly.
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