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Found 4 papers, 2 papers with code
Learning from Models and Data for Visual Grounding
We introduce SynGround, a novel framework that combines data-driven learning and knowledge transfer from various large-scale pretrained models to enhance the visual grounding capabilities of a pretrained vision-and-language model.
Improved Visual Grounding through Self-Consistent Explanations
Vision-and-language models trained to match images with text can be combined with visual explanation methods to point to the locations of specific objects in an image.
Efficient Mirror Detection via Multi-level Heterogeneous Learning
We present HetNet (Multi-level \textbf{Het}erogeneous \textbf{Net}work), a highly efficient mirror detection network.
Weakly-Supervised Camouflaged Object Detection with Scribble Annotations
To achieve this, we first relabel 4, 040 images in existing camouflaged object datasets with scribbles, which takes ~10s to label one image.
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