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Referring Expression Comprehension Using Language Adaptive Inference

AAAI 2023 · Vision-language and multimodal › Human and embodied modeling · approach: Referring expression comprehension · 23 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Use language-conditioned dynamic subnets with joint layer and filter selection instead of a fixed REC networkLADS · RefCOCO, RefCOCO+, RefCOCOg, ReferitPrec@0.5: +2.15%/ +2.67%/ +2.53% on RefCOCO; +2.91%/ +3.96%/ +0.40% on RefCOCO+; +1.01%/ +1.04% on RefCOCOgHelped
Use language-conditioned dynamic subnets with joint layer and filter selection instead of a fixed REC networkLADS · RefCOCO, RefCOCO+, RefCOCOg, ReferitPrec@0.5: +2.53%/ +4.00%/ +0.45% on RefCOCO; +7.66%/ +9.49%/ +4.19% on RefCOCO+; +3.90%/ +4.22% on RefCOCOg; +1.32% on ReferitHelped
Use LADS rather than the reported competing methodsLADS · All datasets (average over referring expressions)inference time (ms): 20msHelped
Pre-train on the large-scale referring-expression dataset before fine-tuningLADS · RefCOCO, RefCOCO+, RefCOCOg, ReferitPrec@0.5Helped
Pre-train on the large-scale referring-expression dataset before fine-tuningLADS · All datasets (average over referring expressions)inference time (ms): 20ms to 18ms; 2ms speedupHelped

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