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Found 4 papers, 3 papers with code
HiVG: Hierarchical Multimodal Fine-grained Modulation for Visual Grounding
Specifically, HiVG consists of a multi-layer adaptive cross-modal bridge and a hierarchical multimodal low-rank adaptation (Hi LoRA) paradigm.
CLIP-VG: Self-paced Curriculum Adapting of CLIP for Visual Grounding
In order to utilize vision and language pre-trained models to address the grounding problem, and reasonably take advantage of pseudo-labels, we propose CLIP-VG, a novel method that can conduct self-paced curriculum adapting of CLIP with pseudo-language labels.
SgVA-CLIP: Semantic-guided Visual Adapting of Vision-Language Models for Few-shot Image Classification
Although significant progress has been made in few-shot learning, most of existing few-shot image classification methods require supervised pre-training on a large amount of samples of base classes, which limits their generalization ability in real world application.
Dynamic-SLAM: Semantic monocular visual localization and mapping based on deep learning in dynamic environment
Then, in view of low recall rate of the existing SSD object detection network, a missed detection compensation algorithm based on the speed invariance in adjacent frames is proposed, which greatly improves the recall rate of detection.
